A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm
---
paper_title: IMAGE SEGMENTATION BY USING EDGE DETECTION

paper_content:

In this paper, we present methods for edge segmentation of satellite images; we used seven techniques for this category; Sobel operator technique, Prewitt technique, Kiresh technique, Laplacian technique, Canny technique, Roberts technique and Edge Maximization Technique (EMT) and they are compared with one another so as to choose the best technique for edge detection segment image. These techniques applied on one satellite images to choose base guesses for segmentation or edge detection image.
---
paper_title: Fast Boundary Detection: A Generalization and a New Algorithm

paper_content:

We study class of fast algorithms that extract object boundaries from digitized images. A set of orthogonal functions related to distinctive image features is presented, which allows efficient extraction of such boundary elements. The properties of these functions are. used to define new criteria for edge detection and a sequential algorithm is presented. Results indicate considerable improvements over, existing techniques, with a very moderate increase of computational cost.
---
paper_title: Edge Detection using Fuzzy Logic in Matlab

paper_content:

This paper proposes the implementation of a very simple but efficient fuzzy logic based algorithm to detect the edges of an image without determining the threshold value. The proposed approach begins by scanning the images using floating 3x3 pixel window. Fuzzy inference system designed has 8 inputs, whic h corresponds to 8 pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is "black", "white" or "edge" pixel. Rule base comprises of sixteen rules, w hich classify the target pixel. The proposed method results for different captured images are compared to those obtained with the linear Sobel operator. Images have always been very important in human life. Soft Co mputing is an emerging field that consists of major seminal theories which include fu zzy logic, genetic algorith ms, evolutionary computation, and neural networks In the last few years there is an increasing interest on using soft computing (SC) techniques to solve image processing real-world problems covering a wide range of domains. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Edge detection i is usually done with a first and/or second derivative measurement following by a comparison with threshold which marks the pixel as either belonging to an edge or not. The result is a binary image wh ich contains only the detected edge pixels. Usage of specific linear time-invariant (LTI) filter is the most common procedure applied to the edge detection problem, and the one which results in the least computational effort. In the case of first-order filters, an edge is interpreted as an abrupt variation in gray level between two neighbor pixels. A very important role is played in image analysis by what are termed feature points, pixels that are identified as having a special property. Feature points include edge pixels as determined by the well-known classic edge detectors of PreWitt, Sobel, Marr, and Canny Recent research has concerned using neural Fuzzy Feature to develop edge detectors, after training on a relatively s mall set of proto-type edges, in sample images classifiable by Classic edge detectors. This work was pioneered by Bezdek et. al, (9) who trained a neural net to give the same fu zzy output as a normalized Sobel Operator. In the system described in (7, 8), all inputs to the fuzzy inference systems (FIS) system are obtained by applying to the original image a high-pass filter, a first- order edge detector filter (Sobel operator) and a low-pass (mean) filter. The whole structure is then tuned to function as a contrast enhancing filter and, in another problem, to segment images in a specified number of input classes. The adopted fuzzy ru les and the fuzzy membership functions are specified according to the kind of filtering to be executed. The work o f this paper is concerned with the development of a Fu zzy logic rules based algorithm for the detection of image edges. By scanning the images using floating 3x3 pixel window mask .Fu zzy In ference based system in MATLAB Environment has been developed, wh ich is capable of detecting edges of an image. The rule -base of 28 rules has been designed to mark the pixel under consideration as Black, White or Edge. The result has been compared with the standard algorithms
---
paper_title: Histogram Modification for Threshold Selection.

paper_content:

Abstract : A standard approach to threshold selection for image segmentation is based on locating valleys in the image's gray level histogram. Several methods have been proposed that produce a transformed histogram in which the valley is deeper, or is converted into a peak, and is thus easier to detect. The transformed histograms used in these methods can all be obtained by creating (gray level, edge value) scatter plots, and computing various weighted projections of these plots on the gray level axis. Using this unified approach makes it easier to understand how the methods work and to predict when a particular method is likely to be effective. The methods are applied to a set of examples involving both real and synthetic images, and the characteristics of the resulting transformed histograms are discussed. (Author)
---
paper_title: Measurement of C-bands in human chromosomes.

paper_content:

Abstract A technique is presented for obtaining quantitative measurements of C-bands in human chromosomes using a computer-controlled microscope and scanner. Relative efficiencies of various procedures for C-band and chromosome area and integrated optical density measurement and the sources and extent of variance in C-band measurements, whether arising from the preparation or measurement techniques or from biological factors, are investigated. Statistical methods for determining whether or not differences in homologue band size can be detected are discussed. Measurements were taken from 50 cells of the same blood culture. Results are given for the number 1 and 16 chromosome C-bands, which appeared heteromorphic on visual inspection. In the case of the number 1 the mean size of each band population was determined to within 8% at the 95% confidence level and the means were found to be significantly different (p
---
paper_title: Threshold Selection, 4.

paper_content:

Abstract : If a picture contains dark objects on a light background, or vice versa, the objects can be separated from the background by thresholding the picture. A good place to choose the threshold is at the average gray level of those picture points where the value of some difference operation is high (e.g., above the p-title, for p = .85 or so). This idea, suggested over then years ago by Yale Katz, is verified for several classes of pictures (handwriting, chromosomes, cloud cover) and various difference operations. Another standard method of choosing a threshold is to examine the histogram of gray levels that occur in the picture. If this has two peaks, corresponding to the gray level ranges of object points and background points, then a good place to choose the threshold is at the bottom of the valley between these peaks. Mason et al recently describes a method of deepening this valley bottom, to make the choice of threshold easier. This method was tested on the pictures mentioned above; it yielded reasonable thresholds, but the valley deepening effect was not as strong as that obtained using other methods.
---
paper_title: BOUNDARY DETECTION OF RADIOGRAPHIC IMAGES BY A THRESHOLD METHOD

paper_content:

Abstract A threshold method based on statistical principles and heuristics is developed to detect boundaries in radiographic images. Each local region of the image containing a portion of boundary is characterized by a mixture of two (normal) intensity distributions. Thresholds are set dynamically according to local, rather than global, characteristics estimated from the observed intensity histograms. A program to implement the method has been written. Experimental results on cardioangiograms are presented to successfully demonstrate the feasibility of the method for low quality images. The method is insensitive to shading or gradually varying interference.
---
paper_title: A Threshold Selection Technique

paper_content:

Threshold selection for picture segmentation is relatively easy when the frequency distribution of gray levels in the picture is strongly bimodal, with the two peaks comparable insize and separated by a deep valley. This report describes a method of handling cases in which the peaks are very unequal in size and the valley is broad. A Laplacian operation is applied to the picture to determine points that lie on or near the edges of objects. Threshold selection becomes easier when the frequency distribution of gray levels of these points is used.
---
paper_title: Automatic boundary detection of the left ventricle from cineangiograms

paper_content:

Abstract A threshold method based on statistical principles and heuristics is developed to detect boundaries in radiographic images. Each local region of the image containing a portion of boundary is characterized by a mixture of two (normal) intensity distributions. Thresholds are set dynamically according to local, rather than global, characteristics estimated from the observed intensity histograms. A program to implement the method has been written. Experimental results on cardioangiograms are presented to successfully demonstrate the feasibility of the method for low quality images. The method is insensitive to shading or gradually varying interference.
---
paper_title: Remote Sensing Image Sequence Segmentation Based on the Modified Fuzzy C-means

paper_content:

Remote sensing image with characteristics of multiple gray level, more informative, fuzzy boundary, complex target structure and so on, there is no completely reliable model to guide the remote sensing image segmentation. In response to these issues, the article presents a remote sensing image sequence segmentation method based on improved FCM (fuzzy c-means) algorithm. The color space selects the lower relevance of HSI (hue, saturation, intensity) and adopts standard covariance matrix-the Mahalanobis distance formula, which is more suitable for the use of remote sensing image. It can solve the initial centers selection problems of fuzzy C-means clustering algorithm by the use of ECM. By using the partition of S component, it can divide the image into high S regions and low S regions. We can do FCM segmentation respectively with H component and I component of these two parts. The segmentation results can be achieved after the merger. The program experimental result shows that this method will enable FCM to converge to global optimal solution with less iteration, and has good stability and robustness. It has good effect on improving the accuracy of threshold segmentation and efficiency for remote sensing images, which can be used for content-based remote sensing image retrieval systems.
---
paper_title: Seeded region growing

paper_content:

We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures. >
---
paper_title: Image Segmentation and Analysis via Multiscale Gradient Watershed Hierarchies

paper_content:

Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scale-space behavior of intensity extrema, edges, intensity ridges, and grey-level blobs. We investigate the multiscale behavior of gradient watershed regions. These regions are defined in terms of the gradient properties of the gradient magnitude of the original image. Boundaries of gradient watershed regions correspond to the edges of objects in an image. Multiscale analysis of intensity minima in the gradient magnitude image provides a mechanism for imposing a scale-based hierarchy on the watersheds associated with these minima. This hierarchy can be used to label watershed boundaries according to their scale. This provides valuable insight into the multiscale properties of edges in an image without following these curves through scale-space. In addition, the gradient watershed region hierarchy can be used for automatic or interactive image segmentation. By selecting subtrees of the region hierarchy, visually sensible objects in an image can be easily constructed.
---
paper_title: A Novel Watershed Image Segmentation Technique using Graceful Labeling

paper_content:

In this paper, we propose a novel watershed image segmentation technique based on absolute difference graph labeling. The main objective is to partition the image into distinct regions that are visually different, homogeneous and meaningful with respect to some characteristics or computed property, that is, such as grey level, texture or color to enable easy image analysis. A graph representing an image is derived from the similarity between the pixels and partitioned by a computationally efficient absolute difference graph method, which identifies representative vertices for each absolute difference and then expands them to obtain complete absolute difference graph labeling. As a result, the image is portioned into a set of disjoint labels (regions).
---
paper_title: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation

paper_content:

A novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is demonstrated. The data to be clustered are represented by an undirected adjacency graph G with arc capacities assigned to reflect the similarity between the linked vertices. Clustering is achieved by removing arcs of G to form mutually exclusive subgraphs such that the largest inter-subgraph maximum flow is minimized. For graphs of moderate size ( approximately 2000 vertices), the optimal solution is obtained through partitioning a flow and cut equivalent tree of G, which can be efficiently constructed using the Gomory-Hu algorithm (1961). However for larger graphs this approach is impractical. New theorems for subgraph condensation are derived and are then used to develop a fast algorithm which hierarchically constructs and partitions a partially equivalent tree of much reduced size. This algorithm results in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree and is able to handle very large graphs with several hundred thousand vertices. The new clustering algorithm is applied to the image segmentation problem. The segmentation is achieved by effectively searching for closed contours of edge elements (equivalent to minimum cuts in G), which consist mostly of strong edges, while rejecting contours containing isolated strong edges. This method is able to accurately locate region boundaries and at the same time guarantees the formation of closed edge contours. >
---
paper_title: Isoperimetric graph partitioning for image segmentation

paper_content:

Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.
---
paper_title: Natural Image Segmentation with Adaptive Texture and Boundary Encoding ?

paper_content:

We present a novel algorithm for unsupervised segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. Our algorithm achieves state-of-the-art results on the Berkeley Segmentation Dataset compared to other popular methods.
---
paper_title: Segmentation of Natural Images by Texture and Boundary Compression

paper_content:

We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes as multi-scale texture features. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. We test our algorithm on the publicly available Berkeley Segmentation Dataset. It achieves state-of-the-art segmentation results compared to other existing methods.
---
paper_title: IMAGE SEGMENTATION BY USING EDGE DETECTION

paper_content:

In this paper, we present methods for edge segmentation of satellite images; we used seven techniques for this category; Sobel operator technique, Prewitt technique, Kiresh technique, Laplacian technique, Canny technique, Roberts technique and Edge Maximization Technique (EMT) and they are compared with one another so as to choose the best technique for edge detection segment image. These techniques applied on one satellite images to choose base guesses for segmentation or edge detection image.
---
paper_title: Benchmarking Image Segmentation Algorithms

paper_content:

We present a thorough quantitative evaluation of four image segmentation algorithms on images from the Berkeley Segmentation Database. The algorithms are evaluated using an efficient algorithm for computing precision and recall with regard to human ground-truth boundaries. We test each segmentation method over a representative set of input parameters, and present tuning curves that fully characterize algorithm performance over the complete image database. We complement the evaluation on the BSD with segmentation results on synthetic images. The results reported here provide a useful benchmark for current and future research efforts in image segmentation.
---
paper_title: A GENETIC BASED APPROACH TO THE TYPE I STRUCTURE IDENTIFICATION PROBLEM

paper_content:

The problem of system input selection, dubbed in the literature as Type I Structure Identification problem, is addressed in this paper using an effective novel method. More specifically, the fuzzy curve technique, introduced by Lin and Cunningham (1995), is extended to an advantageous fuzzy surface technique; the latter is used for fast building a coarse model of the system from a subset of the initial candidate inputs. A simple genetic algorithm, enhanced with a local search operator, is used for finding an optimal subset of necessary and sufficient inputs by considering jointly more than one inputs. Extensive simulation results on both artificial data and real world data have demonstrated comparatively the advantages of the proposed method.
---
paper_title: Image segmentation using evolutionary computation

paper_content:

Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented.
---
paper_title: Application of Genetic Algorithms for Wrapper-based Image Segmentation and Classification

paper_content:

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. This is challenging without any prior knowledge about the object that is being extracted from the scene. We previously proposed a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which provides contextual information regarding the objects to be segmented. Our approach integrated segmentation and classification in a manner analogous to wrapper methods in feature selection. We initially perform low-level segmentation to label the image as a set of non-overlapping blobs. We then use the wrapper framework to select the blobs that comprise the final segmentation based on the classification performance of the wrapper. In this paper, the process of combining the blobs and then evaluating these combinations is performed with a genetic algorithm. We show the performance of the genetic algorithm based wrapper segmentation on real-world complex images of automotive vehicle occupants, where our overall classification accuracy is roughly 88% and the resultant segmentations are extremely accurate.
---
paper_title: The design of different classes of morphological filter using genetic algorithms

paper_content:

Mathematical morphology has produced an important class of non-linear filters. Unfortunately, few design methods exist for these filters. Those existing tending to be computationally intractable or require some expert knowledge of mathematical morphology. The paper describes research into methods by which different classes of morphological filters can be designed by employing genetic algorithms.
---
paper_title: Segmentation of Color Images Using Genetic Algorithms: A Survey

paper_content:

Segmentation subdivides an image into its constituent regions or objects. The level to which the subdivision is carried depends on the problem being solved. Color images can increase the quality of segmentation, but increase the complexity of problem. Genetic a l g o r i t h m s are we ll suited to optimizing complex problems such as image segmentation. This paper gives state- of -art of Genetic Algorithm based segmentation methods. We discuss the f easibi lit y of using genetic algorithms to segment general color images and discuss the issues involved in designing such algorithms. Keywords - Color image, Genetic algorithms, Segmentation
---
paper_title: Robust image segmentation using genetic algorithm with a fuzzy measure

paper_content:

In this paper we present new region-based image segmentation methodology on gray-level images using a genetic algorithm with a fuzzy measure. We first propose a fuzzy validity function which measures a degree of separation and compactness between and within finely segmented regions, and an edge strength along boundaries of all regions. We apply the genetic algorithm to search a good or usable region segmentation, which maximizes the quality of regions generated by split- and-merge processing. The iterative algorithm provides a useful method for image segmentation without the need for critical parameters or threshold values, iterative visual interaction or a priori knowledge of an image.
---
paper_title: Adaptive image segmentation using a genetic algorithm

paper_content:

We present the first closed loop image segmentation system which incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem is formulated as an optimization problem and the genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. The goals of our adaptive image segmentation system are to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. We present experimental results which demonstrate learning and the ability to adapt the segmentation performance in outdoor color imagery.
---
paper_title: The Phoenix Image Segmentation System: Description and Evaluation

paper_content:

Abstract : PHOENIX is a computer program for segmenting images into homogeneous closed regions. It uses histogram analysis, thresholding and connected-components analysis to produce a partial segmentation, then resegments each region until various stopping criteria are satisfied. Its major contributions over other recursive segmenters are a sophisticated control interface, optional use of more than one histogram-dependent intensity threshold during tentative segmentation of each region. and spatial analysis of resulting subregions as a form of "look-ahead" for choosing between promising spectral features at each step. PHOENIX was contributed to the DARPA Image Understanding Testbed at SRI by Carnegie-Mellon University. This report summarizes application for which PHOENIX is suited, the history and nature of the algorithm, details of the Testbed implementation, the manner in which PHOENIX is invoked and controlled, the type of results that can be expected, and suggestions for further development. Baseline parameter sets are given for producing reasonable segmentations of typical imagery.
---
paper_title: Genetic algorithms for object recognition in a complex scene

paper_content:

A realworld computer vision module must deal with a wide variety of environmental parameters. Object recognition, one of the major tasks of this vision module, typically requires a preprocessing step to locate objects in the scenes that ought to be recognized. Genetic algorithms are a search technique for dealing with a very large search space, such as the one encountered in image segmentation or object recognition. The article describes a technique for using genetic algorithms to combine the image segmentation and object recognition steps for a complex scene. The results show that this approach is a viable method for successfully combining the image segmentation and object recognition steps for a computer vision module.
---
paper_title: A Parallel Genetic Algorithm for Cell Image Segmentation

paper_content:

Abstract In this paper, we propose a parallel genetic algorithm for cell image segmentation under severe noise. Our contribution aims at overcoming the drawback of the slow convenence of the traditional genetic algorithm, which was used in our previous work. A priori knowledge about cell shape is incorporated in our method. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. We firstly obtain the gradient image using Canny edge detector; and then use kernel-based dynamic clustering to find out the image points that have a high probability belonging to each cell. Finally a parallel genetic algorithm is used to adjust the parameters of the cell contour model to find a best matching. The segmentation results of noisy human thyroid and small intestine cell images demonstrate that the proposed method is very successful in segmenting images of elliptically shaped cells.
---
paper_title: A hierarchical distributed genetic algorithm for image segmentation

paper_content:

A novel hierarchical distributed genetic algorithm is proposed for image segmentation. Firstly, a technique of histogram dichotomy is proposed to explore the statistical property of input image and produce a hierarchical quantization image. Then a hierarchical distributed genetic algorithm (HDGA) is imposed on the quantized image to explore the spatial connectivity and produce final segmentation result. HDGA is a major improvement of the original distributed genetic algorithm (DGA) and multiscale distributed genetic algorithm (MDGA) in four aspects: (1) HDGA does not require the a priori number of image regions, however it can effectively and adaptively control the segmentation quality; (2) the chromosome structure is revised from the original label (multilabel)-condition-fitness format to a more compact (storage-efficient) label-fitness format; (3) the fitness function is revised to utilize the spatial connectivity, but not the original "reconstruction" error; (4) three revised genetic operations are presented to make the algorithm computation-efficient. Our experiments give proofs for the advantages of HDGA.
---
paper_title: Multi-Scale Gradient Magnitude Watershed Segmentation

paper_content:

A partitioning of an nD image is defined as the watersheds of some locally computable inhomogeneity measure. Dependent on the scale of the inhomogeneity measure a coarse or fine partitioning is defined. By analysis of the structural changes (catastrophes) in the measure introduced when scale is increased, a multi-scale linking of segments can be defined. This paper describes the multi-scale linking based on recent results of the deep structure of the squared gradient field[1]. An interactive semi-automatic segmentation tool, and results on synthetic and real 3D medical images are presented.
---
paper_title: Image Colour Segmentation by Genetic Algorithms

paper_content:

Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. In this work, a method is described for evolving adaptive procedures for these problems. In many real world applications data clustering constitutes a fundamental issue whenever behavioural or feature domains can be mapped into topological domains. We formulate the segmentation problem upon such images as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into Genetic Algorithms, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its intrinsic NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of Genetic Algorithms to automatic and unsupervised texture segmentation. Some examples in Colour Maps, Ornamental Stones and in Human Skin Mark segmentation are presented and overall results discussed. KEYWORDS: Genetic Algorithms, Colour Image Segmentation, Classification, Clustering.
---
paper_title: Adaptive integrated image segmentation and object recognition

paper_content:

The paper presents a general approach to image segmentation and object recognition that can adapt the image segmentation algorithm parameters to the changing environmental conditions. Segmentation parameters are represented by a team of generalized stochastic learning automata and learned using connectionist reinforcement learning techniques. The edge-border coincidence measure is first used as reinforcement for segmentation evaluation to reduce computational expenses associated with model matching during the early stage of adaptation. This measure alone, however, cannot reliably predict the outcome of object recognition. Therefore, it is used in conjunction with model matching where the matching confidence is used as a reinforcement signal to provide optimal segmentation evaluation in a closed-loop object recognition system. The adaptation alternates between global and local segmentation processes in order to achieve optimal recognition performance. Results are presented for both indoor and outdoor color images where the performance improvement over time is shown for both image segmentation and object recognition.
---
paper_title: Application of Genetic Algorithms for Wrapper-based Image Segmentation and Classification

paper_content:

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. This is challenging without any prior knowledge about the object that is being extracted from the scene. We previously proposed a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which provides contextual information regarding the objects to be segmented. Our approach integrated segmentation and classification in a manner analogous to wrapper methods in feature selection. We initially perform low-level segmentation to label the image as a set of non-overlapping blobs. We then use the wrapper framework to select the blobs that comprise the final segmentation based on the classification performance of the wrapper. In this paper, the process of combining the blobs and then evaluating these combinations is performed with a genetic algorithm. We show the performance of the genetic algorithm based wrapper segmentation on real-world complex images of automotive vehicle occupants, where our overall classification accuracy is roughly 88% and the resultant segmentations are extremely accurate.
---
paper_title: Selectionist relaxation: genetic algorithms applied to image segmentation

paper_content:

This paper describes an unsupervised image segmentation method based on a fine-grained distributed genetic algorithm. Unlike other proposed applications of genetic algorithms to this problem, the method does not require the definition of an objective fitness function evaluating candidate segmentation results. The output segmentation instead emerges as a by-product of the evolution of a population of chromosomes that are mapped onto the image and that locally adapt to its features. A sketchy analysis of the algorithm is proposed, according to which the optimal GA parameters can be predicted. The predictions are experimentally tested on artificial data. Results obtained on natural data are reported and compared with the output of a standard region segmentation method.
---
paper_title: Tuning Range Image Segmentation by Genetic Algorithm

paper_content:

Several range image segmentation algorithms have been proposed, each one to be tuned by a number of parameters in order to provide accurate results on a given class of images. Segmentation parameters are generally affected by the type of surfaces (e.g., planar versus curved) and the nature of the acquisition system (e.g., laser range finders or structured light scanners). It is impossible to answer the question, which is the best set of parameters given a range image within a class and a range segmentation algorithm? Systems proposing such a parameter optimization are often based either on careful selection or on solution space-partitioning methods. Their main drawback is that they have to limit their search to a subset of the solution space to provide an answer in acceptable time. In order to provide a different automated method to search a larger solution space, and possibly to answer more effectively the above question, we propose a tuning system based on genetic algorithms. A complete set of tests was performed over a range of different images and with different segmentation algorithms. Our system provided a particularly high degree of effectiveness in terms of segmentation quality and search time.
---
paper_title: A wrapper-based approach to image segmentation and classification

paper_content:

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications.
---
paper_title: A wrapper-based approach to image segmentation and classification

paper_content:

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. This is extremely difficult without any prior knowledge about the object that is being extracted from the scene. We propose a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which provide contextual information regarding the objects to be segmented. We note that traditional segmentation can then be viewed as a filter operating on the image independently of the classifier, much like the filter methods for feature selection. Our motivation for integrating segmentation and classification follows the wrapper methods of feature selection. In the wrapper methods for feature selection, the classifier is an integral part of the selection process and serves as the metric to decide the best feature set. In the same way, we wrap the segmentation and classification together, and use the classification accuracy as the metric to determine the best segmentation. We show the performance of wrapper-based segmentation on real-world and complex images of automotive vehicle occupants.
---
paper_title: Closed-loop object recognition using reinforcement learning

paper_content:

Current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. This is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. The use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. The system is verified through experiments on sequences of color images with varying external conditions.
---
paper_title: A Wrapper-based Feature Selection for Analysis of Large Data Sets

paper_content:

Knowledge discovery from large data sets using classic data mining techniques has been proved to be difficult due to large size in both dimension and samples. In real appli- cations, data sets often consist of many noisy, redundant, and irrelevant features, resulting in degrading the classification accuracy and increasing the complexity exponentially. Due to the inherent nature, the analysis of the quality of data sets is difficult and very limited approaches about this issue can be found in the literature. This paper presents a novel method to investigate the quality and structure of data sets, i.e., how to analyze whether there are noisy and irrelevant features em- bedded in data sets. In doing so, a wrapper-based feature se- lection method using genetic algorithm and an external clas- sifier are employed for selecting the discriminative features. The importance of features are ranked in terms of their fre- quency appeared in the selected chromosomes. The effective- ness of proposed idea has been investigated and discussed with some sample data sets. large high dimensional data sets. In addition, the complexity grows exponentially with the increase of the number of fea- tures, making the search of all possible spaces infeasible. Traditional classification/clustering methods try to group clusters in full-dimensional space with either distance-based measure or density-based measure, including k-means clus- tering, self-organizing maps (SOM), and hierarchical me- thods, and so on (5). Normally, the large data sets such as microarray data are much larger in dimensionality than some sample data sets used in conventional data mining algorithms. Moreover, they consist of much noises, redundant, and irre- levant attributes due to the natural variation, and high inter- nal dependences between features in real applications. Se- lecting fewer discriminative attributes by removing irrelevant and/or redundant attributes can not only increase the classifi- cation accuracy but also reduce the computational complexi- ty to alleviate 'the curse of dimensionality' problem. In this respect, the investigation of the quality and structure of data sets is of paramount importance to the success of data mining algorithms.
---
paper_title: Multicomponent Image Segmentation Using a Genetic Algorithm and Artificial Neural Network

paper_content:

Image segmentation is an essential process for image analysis. Several methods were developed to segment multicomponent images, and the success of these methods depends on several factors including (1) the characteristics of the acquired image and (2) the percentage of imperfections in the process of image acquisition. The majority of these methods require a priori knowledge, which is difficult to obtain. Furthermore, they assume the existence of models that can estimate its parameters and fit to the given data. However, such a parametric approach is not robust, and its performance is severely affected by the correctness of the utilized parametric model. In this letter, a new multicomponent image segmentation method is developed using a nonparametric unsupervised artificial neural network called Kohonen's self-organizing map (SOM) and hybrid genetic algorithm (HGA). SOM is used to detect the main features that are present in the image; then, HGA is used to cluster the image into homogeneous regions without any a priori knowledge. Experiments that are performed on different satellite images confirm the efficiency and robustness of the SOM-HGA method compared to the Iterative Self-Organizing DATA analysis technique (ISODATA).
---
paper_title: Robust image segmentation using genetic algorithm with a fuzzy measure

paper_content:

In this paper we present new region-based image segmentation methodology on gray-level images using a genetic algorithm with a fuzzy measure. We first propose a fuzzy validity function which measures a degree of separation and compactness between and within finely segmented regions, and an edge strength along boundaries of all regions. We apply the genetic algorithm to search a good or usable region segmentation, which maximizes the quality of regions generated by split- and-merge processing. The iterative algorithm provides a useful method for image segmentation without the need for critical parameters or threshold values, iterative visual interaction or a priori knowledge of an image.
---
paper_title: A Genetic C-Means Clustering Algorithm Applied To Color Image Quantization

paper_content:

This paper describes a novel data clustering algorithm, which is a hybrid approach combining a genetic algorithm with the classical c-means clustering algorithm (CMA). The proposed technique is superior to CMA in the sense that it converges to a nearby global optimum rather than a local one. As an application, the problem of color image quantization is elaborated. Here, it is shown that substantial improvement of image quality is obtained by using the genetic approach.
---
paper_title: Genetic algorithm based feature selection for target detection in SAR images

paper_content:

Abstract A genetic algorithm (GA) approach is presented to select a set of features to discriminate the targets from the natural clutter false alarms in SAR images. Four stages of an automatic target detection system are developed: the rough target detection, feature extraction from the potential target regions, GA based feature selection and the final Bayesian classification. A new fitness function based on minimum description length principle (MDLP) is proposed to drive GA and it is compared with three other fitness functions. Experimental results show that the new fitness function outperforms the other three fitness functions and the GA driven by it selected a good subset of features to discriminate the targets from clutters effectively.
---
paper_title: A Parallel Genetic Algorithm for Cell Image Segmentation

paper_content:

Abstract In this paper, we propose a parallel genetic algorithm for cell image segmentation under severe noise. Our contribution aims at overcoming the drawback of the slow convenence of the traditional genetic algorithm, which was used in our previous work. A priori knowledge about cell shape is incorporated in our method. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. We firstly obtain the gradient image using Canny edge detector; and then use kernel-based dynamic clustering to find out the image points that have a high probability belonging to each cell. Finally a parallel genetic algorithm is used to adjust the parameters of the cell contour model to find a best matching. The segmentation results of noisy human thyroid and small intestine cell images demonstrate that the proposed method is very successful in segmenting images of elliptically shaped cells.
---
paper_title: Image Colour Segmentation by Genetic Algorithms

paper_content:

Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. In this work, a method is described for evolving adaptive procedures for these problems. In many real world applications data clustering constitutes a fundamental issue whenever behavioural or feature domains can be mapped into topological domains. We formulate the segmentation problem upon such images as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into Genetic Algorithms, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its intrinsic NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of Genetic Algorithms to automatic and unsupervised texture segmentation. Some examples in Colour Maps, Ornamental Stones and in Human Skin Mark segmentation are presented and overall results discussed. KEYWORDS: Genetic Algorithms, Colour Image Segmentation, Classification, Clustering.
---
paper_title: Color image segmentation with genetic algorithm for in-field weed sensing.

paper_content:

This study was undertaken to develop machine vision-based weed detection technology for outdoor natural lighting conditions. Supervised color image segmentation using a binary-coded genetic algorithm (GA) identifying a region in Hue-Saturation-Intensity (HSI) color space (GAHSI) for outdoor field weed sensing was successfully implemented. Images from two extreme intensity lighting conditions, those under sunny and cloudy sky conditions, were mosaicked to explore the possibility of using GAHSI to locate a plant region in color space when these two extremes were ::: presented simultaneously. The GAHSI result provided evidence for the existence and separability of such a region. In the experiment, GAHSI performance was measured by comparing the GAHSI-segmented image with a corresponding hand-segmented reference image. When compared with cluster analysis-based segmentation results, the GAHSI achieved ::: equivalent performance.
---

Title: A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

Section 1: INTRODUCTION
Description 1: Write an introduction to image segmentation and its importance, including the role of Genetic Algorithms in optimizing segmentation.

Section 2: IMAGE SEGMENTATION METHODOLOGIES
Description 2: Discuss various image segmentation methodologies including Edge Detection Technique, Thresholding Technique, Region Growing Based Technique, Watershed Technique, Histogram based Segmentation, Graph Partitioning Method, and Compression Based Methods.

Section 3: BENCHMARKING IMAGE SEGMENTATION
Description 3: Describe the process and methods used to benchmark image segmentation algorithms.

Section 4: GENETIC ALGORITHM
Description 4: Explain Genetic Algorithms, their historical development, and their application in optimizing image segmentation parameters and region labeling.

Section 5: WRAPPER APPROACH USING GAUSSIAN ESTIMATOR
Description 5: Discuss the wrapper approach utilizing a Gaussian Estimator for image segmentation and its advantages over traditional methods.

Section 6: FUTURE WORK
Description 6: Outline future research directions and improvements in image segmentation algorithms highlighted by multiple studies, with a focus on Genetic Algorithms.

Section 7: RESULT
Description 7: Present the results of the proposed image segmentation algorithm using Genetic Algorithms, including the methodology, comparison with other algorithms, and implementation details.

Section 8: CONCLUSION
Description 8: Summarize the findings of the paper, the advancements in image segmentation methodologies, the proposed wrapper framework with Genetic Algorithms, and the importance of classifier accuracy in improving segmentation results.

Section 9: ACKNOWLEDGMENTS
Description 9: Acknowledge contributions of individuals and institutions that supported the research.