Texture image analysis and texture classification methods - A review
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paper_title: Texture features for browsing and retrieval of image data

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Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.
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paper_title: Grey level co-occurrence matrix and its application to seismic data

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Christoph Georg Eichkitz, John Davies, Johannes Amtmann, Marcellus Gregor Schreilechner and Paul de Groot demonstrate how grey level co-occurrence matrix can be adapted to work on 3D imaging of seismic data. ::: ::: Texture analysis is the extraction of textural features from images (Tuceryan and Jain, 1998). The meaning of texture varies, depending on the area of science in which it is used. In general, texture refers to the physical character of an object or the appearance of an image. In image analysis, texture is defined as a function of the spatial variation in intensities of pixels (Tuceryan and Jain, 1998). Seismic texture refers to the magnitude and variability of neighbouring amplitudes at sample locations and is physically related to the distribution of scattering objects (geological texture) within a small volume at the corresponding subsurface location (Gao, 2008). Four principal methods have been developed for the analysis of seismic texture (Figure 1). These are texture classification, segmentation, synthesis, and shape.
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paper_title: Texture features for browsing and retrieval of image data

paper_content:

Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.
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paper_title: Grey level co-occurrence matrix and its application to seismic data

paper_content:

Christoph Georg Eichkitz, John Davies, Johannes Amtmann, Marcellus Gregor Schreilechner and Paul de Groot demonstrate how grey level co-occurrence matrix can be adapted to work on 3D imaging of seismic data. ::: ::: Texture analysis is the extraction of textural features from images (Tuceryan and Jain, 1998). The meaning of texture varies, depending on the area of science in which it is used. In general, texture refers to the physical character of an object or the appearance of an image. In image analysis, texture is defined as a function of the spatial variation in intensities of pixels (Tuceryan and Jain, 1998). Seismic texture refers to the magnitude and variability of neighbouring amplitudes at sample locations and is physically related to the distribution of scattering objects (geological texture) within a small volume at the corresponding subsurface location (Gao, 2008). Four principal methods have been developed for the analysis of seismic texture (Figure 1). These are texture classification, segmentation, synthesis, and shape.
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paper_title: Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data

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Abstract This paper contributes empirical evidence about the usefulness of remote sensing imagery to quantify the degree of poverty at the intra-urban scale. This concept is based on two premises: first, that the physical appearance of an urban settlement is a reflection of the society; and second, that the people who reside in urban areas with similar physical housing conditions have similar social and demographic characteristics. We use a very high spatial resolution (VHR) image from one of the most socioeconomically divergent cities in the world, Medellin (Colombia), to extract information on land cover composition using per-pixel classification and on urban texture and structure using an automated tool for texture and structure feature extraction at object level. We evaluate the potential of these descriptors to explain a measure of poverty known as the Slum Index. We found that these variables explain up to 59% of the variability in the Slum Index. Similar approaches could be used to lower the cost of socioeconomic surveys by developing an econometric model from a sample and applying that model to the rest of the city and to perform intercensal or intersurvey estimates of intra-urban Slum Index maps.
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paper_title: Multispectral Texture Features from Visible and Near-Infrared Synthetic Face Images for Face Recognition

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Recently, high-performance face recognition has attracted research attention in real-world scenarios. Thanks to the advances in sensor technology, face recognition system equipped with multiple sensors has been widely researched. Among them, face recognition system with near-infrared imagery has been one important research topic. In this paper, complementary effect resided in face images captured by nearinfrared and visible rays is exploited by combining two distinct spectral images (i.e., face images captured by near-infrared and visible rays). We propose a new texture feature (i.e., multispectral texture feature) extraction method with synthesized face images to achieve high-performance face recognition with illumination-invariant property. The experimental results show that the proposed method enhances the discriminative power of features thanks the complementary effect.
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paper_title: Particle filter based on joint color texture histogram for object tracking

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Particle filter has grown to be a standard tool for solving visual tracking problems in real world applications. One of the critical tasks in object tracking is the tracking of fast-moving objects in complex environments, which contain cluttered background and scale change. In this paper, a new tracking algorithm is presented by using the joint color texture histogram to represent a target and then applying it to particle filter algorithm called PFJCTH. The texture features of the object are extracted by using the local binary pattern (LBP) technique to represent the object. The proposed algorithm extracts effectively the edge and corner features in the target region, which characterize better and represent more robustly the target. The experiments showed that this new proposed algorithm produces excellent tracking results and outperforms other tracking algorithms.
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paper_title: Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images

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Breast cancer is one of the major causes of death for women in the last decade. Thermography is a breast imaging technique that can detect cancerous masses much faster than the conventional mammography technology. In this paper, a breast cancer detection algorithm based on asymmetric analysis as primitive decision and decision-level fusion by using Hidden Markov Model (HMM) is proposed. In this decision structure, by using primitive decisions obtained from extracted features from left and right breasts and also asymmetric analysis, final decision is determined by a new application of HMM. For this purpose, a novel texture feature based on Markov Random Field (MRF) model that is named MRF-based probable texture feature and another texture feature based on a new scheme in Local Binary Pattern (LBP) of the images are extracted. In the MRF-based probable texture feature, we try to capture breast texture information by using proper definition of neighborhood system and clique and also determination of new potential functions. Ultimately, our proposed breast cancer detection algorithm is evaluated on a variety dataset of thermography images and false negative rate of 8.3% and false positive rate of 5% are obtained on test image dataset. We propose a two-stage breast cancer detection algorithm by decision-level fusion.We tried to improve false accept of previous algorithms by our proposed algorithm.We used Hidden Markov Model as a fusion algorithm to fuse primitive decisions.We propose a novel texture feature based on Markov Random Field model.To extract color and edge information of images, we modified Local Binary Pattern.
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paper_title: Genetic based LBP feature extraction and selection for facial recognition

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This paper presents a novel approach to LBP feature extraction. Unlike other LBP feature extraction methods, we evolve the number, position, and the size of the areas of feature extraction. The approach described in this paper also attempts to minimize the number of areas as well as the size in an effort to reduce the total number of features needed for LBP-based face recognition. In addition to reducing the number of features by 63%, our approach also increases recognition accuracy from an average of 99.04% to 99.84%.
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paper_title: Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor

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This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.
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paper_title: Face Recognition Using Local Binary Patterns (LBP)

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The face of a human being conveys a lot of information about identity and emotional state of the person. Face recognition is an interesting and challenging problem, and impacts important applications in many areas such as identification for law enforcement, authentication for banking and security system access, and personal identification among others. In our research work mainly consists of three parts, namely face representation, feature extraction and classification. Face representation represents how to model a face and determines the successive algorithms of detection and recognition. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database. In our research work, we empirically evaluate face recognition which considers both shape and texture information to represent face images based on Local Binary Patterns for person-independent face recognition. The face area is first divided into small regions from which Local Binary Patterns (LBP), histograms are extracted and concatenated into a single feature vector. This feature vector forms an efficient representation of the face and is used to measure similarities between images.
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paper_title: Image texture as a remotely sensed measure of vegetation structure

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Abstract Ecologists commonly collect data on vegetation structure, which is an important attribute for characterizing habitat. However, measuring vegetation structure across large areas is logistically difficult. Our goal was to evaluate the degree to which sample-point pixel values and image texture of remotely sensed data are associated with vegetation structure in a North American grassland–savanna–woodland mosaic. In the summers of 2008–2009 we collected vegetation structure measurements at 193 sample points from which we calculated foliage-height diversity and horizontal vegetation structure at Fort McCoy Military Installation, Wisconsin, USA. We also calculated sample-point pixel values and first- and second-order image texture measures, from two remotely sensed data sources: an infrared air photo (1-m resolution) and a Landsat TM satellite image (30-m resolution). We regressed foliage-height diversity against, and correlated horizontal vegetation structure with, sample-point pixel values and texture measures within and among habitats. Within grasslands, savanna, and woodland habitats, sample-point pixel values and image texture measures explained 26–60% of foliage-height diversity. Similarly, within habitats, sample-point pixel values and image texture measures were correlated with 40–70% of the variation of horizontal vegetation structure. Among habitats, the mean of the texture measure ‘second-order contrast’ from the air photo explained 79% of the variation in foliage-height diversity while ‘first-order variance’ from the air photo was correlated with 73% of horizontal vegetation structure. Our results suggest that sample-point pixel values and image texture measures calculated from remotely sensed data capture components of foliage-height diversity and horizontal vegetation structure within and among grassland, savanna, and woodland habitats. Vegetation structure, which is a key component of animal habitat, can thus be mapped using remotely sensed data.
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paper_title: Fabric defect detection based on GLCM and Gabor filter: A comparison

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Abstract Fabric defect detection has been an active area of research since a long time and still a robust system is needed which can fulfill industrial requirements. A robust automatic fabric defect detection system (FDDS) would results in quality products and more revenues. Many different approaches and method have been tried to implement FDDS. Most of them are based on two approaches, one is statistical like gray level co-occurrence (GLCM) and other is transform based like Gabor filter. This paper presents a new scheme for automated FDDS implementation using GLCM and also compare it with Gabor filter approach. GLCM texture statistics are extracted and plotted against the inter-pixel distance of GLCM as signal graph. The non-defective fabric image information is compared with the test fabric image. In Gabor filter based approach, a bank of Gabor filter with different scales and orientations is generated and fabric images are filtered with convolution mask. The generated magnitude responses are compared for defect decision. In our implementation of both approaches in same environment, the GLCM approach produces higher defect detection accuracies than Gabor filter approach and more computationally efficient.
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paper_title: Textural Features for Image Classification

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Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
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paper_title: Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images

paper_content:

Breast cancer is one of the major causes of death for women in the last decade. Thermography is a breast imaging technique that can detect cancerous masses much faster than the conventional mammography technology. In this paper, a breast cancer detection algorithm based on asymmetric analysis as primitive decision and decision-level fusion by using Hidden Markov Model (HMM) is proposed. In this decision structure, by using primitive decisions obtained from extracted features from left and right breasts and also asymmetric analysis, final decision is determined by a new application of HMM. For this purpose, a novel texture feature based on Markov Random Field (MRF) model that is named MRF-based probable texture feature and another texture feature based on a new scheme in Local Binary Pattern (LBP) of the images are extracted. In the MRF-based probable texture feature, we try to capture breast texture information by using proper definition of neighborhood system and clique and also determination of new potential functions. Ultimately, our proposed breast cancer detection algorithm is evaluated on a variety dataset of thermography images and false negative rate of 8.3% and false positive rate of 5% are obtained on test image dataset. We propose a two-stage breast cancer detection algorithm by decision-level fusion.We tried to improve false accept of previous algorithms by our proposed algorithm.We used Hidden Markov Model as a fusion algorithm to fuse primitive decisions.We propose a novel texture feature based on Markov Random Field model.To extract color and edge information of images, we modified Local Binary Pattern.
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paper_title: A comparative study of texture measures with classification based on featured distributions

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This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

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The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: Object recognition from local scale-invariant features

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An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

paper_content:

The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: Texture Classification using Curvelet Transform

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Texture classification has played an important role in many real life applications. Now, classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2-D is introduced. But images often contain curves rather than straight lines, so curvelet transform is designed to handle it. It allows representing edges and other singularities along lines in a more efficient way when compared with other transforms. In this paper, the issue of texture classification based on curvelet transform has been analyzed. One group feature vector can be constructed by the mean and variance of the curvelet statistical features, which are derived from the sub-bands of the curvelet decomposition and are used for classification. Experimental results show that this approach allows obtaining high degree of success rate in classification.
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paper_title: Texture classification using Gabor filters

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Abstract An unsupervised texture classification scheme is proposed in this paper. The texture features are based on the image local spectrum which is obtained by a bank of Gabor filters. The fuzzy clustering algorithm is used for unsupervised classification. In many applications, this algorithm depends on assumptions made about the number of subgroups present in the data. Therefore we discuss ideas behind cluster validity measures and propose a method for choosing the optimal number of clusters.
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paper_title: Texture classification using ridgelet transform

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Texture classification has long been an important research topic in image processing. Classification based on the wavelet transform has become very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, a ridgelet transform which deals effectively with line singularities in 2-D is introduced. It allows representing edges and other singularities along lines in a more efficient way. In this paper, the issue of texture classification based on a ridgelet transform has been analyzed. Features are derived from sub-bands of the ridgelet decomposition and are used for classification for a data set containing 20 texture images. Experimental results show that this approach allows to obtain a high degree of success in classification.
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paper_title: Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression

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A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D Gabor representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations, which provide a complete image description in terms of locally windowed 2-D spectral coordinates embedded within global 2-D spatial coordinates. In the present neural network approach, based on interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights, the network finds coefficients for complete conjoint 2-D Gabor transforms without restrictive conditions. In wavelet expansions based on a biologically inspired log-polar ensemble of dilations, rotations, and translations of a single underlying 2-D Gabor wavelet template, image compression is illustrated with ratios up to 20:1. Also demonstrated is image segmentation based on the clustering of coefficients in the complete 2-D Gabor transform. >
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paper_title: Unsupervised texture segmentation using Gabor filters

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Abstract This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the “true” number of texture categories.
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paper_title: Multi-scale gray level co-occurrence matrices for texture description

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Abstract Texture information plays an important role in image analysis. Although several descriptors have been proposed to extract and analyze texture, the development of automatic systems for image interpretation and object recognition is a difficult task due to the complex aspects of texture. Scale is an important information in texture analysis, since a same texture can be perceived as different texture patterns at distinct scales. Gray level co-occurrence matrices (GLCM) have been proved to be an effective texture descriptor. This paper presents a novel strategy for extending the GLCM to multiple scales through two different approaches, a Gaussian scale-space representation, which is constructed by smoothing the image with larger and larger low-pass filters producing a set of smoothed versions of the original image, and an image pyramid, which is defined by sampling the image both in space and scale. The performance of the proposed approach is evaluated by applying the multi-scale descriptor on five benchmark texture data sets and the results are compared to other well-known texture operators, including the original GLCM, that even though faster than the proposed method, is significantly outperformed in accuracy.
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paper_title: Binary Gabor pattern: An efficient and robust descriptor for texture classification

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In this paper, we present a simple yet efficient and effective multi-resolution approach to gray-scale and rotation invariant texture classification. Given a texture image, we at first convolve it with J Gabor filters sharing the same parameters except the parameter of orientation. Then by binarizing the obtained responses, we can get J bits at each location. Then, each location can be assigned a unique integer, namely “rotation invariant binary Gabor pattern (BGP ri )”, formed from J bits associated with it using some rule. The classification is based on the image's histogram of its BGP ri s at multiple scales. Using BGP ri , there is no need for a pre-training step to learn a texton dictionary, as required in methods based on clustering such as MR8. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of BGP ri over the other state-of-the-art texture representation methods evaluated. The Matlab source codes are publicly available at http://sse.tongji.edu.cn/linzhang/IQA/BGP/BGP.htm
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paper_title: Local spiking pattern and its application to rotation- and illumination-invariant texture classification

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Abstract Automatic classification of texture images is an important and challenging task in the applications of image analysis and scene understanding. In this paper, we focus on the problem of the classification of texture images acquired under various rotation and illumination conditions and propose a new local image descriptor which is named local spiking pattern (LSP). Specifically, the proposed LSP uses a 2-dimensional neural network, which is made up of a series of interconnected spiking neurons, to generate binary images by iteration. The binary images are then encoded to generate discriminative feature vectors. In classification phase, we use a nearest neighborhood classifier to achieve supervised classification. Finally, LSP is evaluated by comparison with some state-of-the-art local image descriptors. Experimental results on Outex texture database show that LSP outperforms most of the other local image descriptors in the noiseless case and shows high robustness when texture images are distorted by salt & pepper noise.
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paper_title: Local spiking pattern and its application to rotation- and illumination-invariant texture classification

paper_content:

Abstract Automatic classification of texture images is an important and challenging task in the applications of image analysis and scene understanding. In this paper, we focus on the problem of the classification of texture images acquired under various rotation and illumination conditions and propose a new local image descriptor which is named local spiking pattern (LSP). Specifically, the proposed LSP uses a 2-dimensional neural network, which is made up of a series of interconnected spiking neurons, to generate binary images by iteration. The binary images are then encoded to generate discriminative feature vectors. In classification phase, we use a nearest neighborhood classifier to achieve supervised classification. Finally, LSP is evaluated by comparison with some state-of-the-art local image descriptors. Experimental results on Outex texture database show that LSP outperforms most of the other local image descriptors in the noiseless case and shows high robustness when texture images are distorted by salt & pepper noise.
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paper_title: Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features

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Texture classification became one of the problems which has been paid much attention on by image processing scientists since late 80s. Consequently, since now many different methods have been proposed to solve this problem. In most of these methods the researchers attempted to describe and discriminate textures based on linear and non-linear patterns. The linear and non-linear patterns on any window are based on formation of Grain Components in a particular order. Grain component is a primitive unit of morphology that most meaningful information often appears in the form of occurrence of that. The approach which is proposed in this paper could analyze the texture based on its grain components and then by making grain components histogram and extracting statistical features from that would classify the textures. Finally, to increase the accuracy of classification, proposed approach is expanded to color images to utilize the ability of approach in analyzing each RGB channels, individually. Although, this approach is a general one and it could be used in different applications, the method has been tested on the stone texture and the results can prove the quality of approach.
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paper_title: SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains

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The paper presents a new image classification technique which first extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains. Subsequently, it exploits a support vector machine (SVM) to perform image texture classification. For convenience, it is called the SRITCSD method hereafter. First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image. Also, the SRITCSD method employs the SVM to serve as a multiclassifier for image texture features. Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM. The experimental results demonstrate that the SRITCSD method can achieve satisfying results and outperform other existing methods under considerations here. The following structure displays the conceptual design of the SRITCSD method for image texture classification. More specifically, it depicts the structure of the training phase and the testing phase for the SRITCSD method. In the training phase, the DWT-FE component denotes the feature-extraction scheme applied for a DWT version image. The feature set, f D W T j , in Eq. (8) is computed via feeding the DWT-FE component with a DWT version image. Let I S V D j represent that image I j is enhanced via the SVD. Another feature set, f S V D , D W T j , in Eq. (11) is calculated via feeding the DWT-FE component with a DWT version of I S V D j . Also, the SVM performs as a multiclassifier with respect to a set of training patterns which are constructed using image texture features, f D W T j and f S V D , D W T j . Meanwhile, the PSO algorithm is employed to optimize the SRITCSD method, which selects the nearly optimal combination of features and a set of parameters utilized in the SVM. In the testing phase of the SRITCSD method, two feature sets, f D W T q and f S V D , D W T q , are computed for a query image I q . The classification result can be obtained via feeding the trained SVM model with f S V D , D W T q to estimate which category the image I q belongs to.Display Omitted The paper presents an image classification technique which extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains.First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image.Also, the SVM serves as a multiclassifier for image texture features.Meanwhile, the particle swarm optimization (PSO) algorithm is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM.The experimental results demonstrate that the method can achieve satisfying results and outperform other existing methods.
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paper_title: SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains

paper_content:

The paper presents a new image classification technique which first extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains. Subsequently, it exploits a support vector machine (SVM) to perform image texture classification. For convenience, it is called the SRITCSD method hereafter. First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image. Also, the SRITCSD method employs the SVM to serve as a multiclassifier for image texture features. Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM. The experimental results demonstrate that the SRITCSD method can achieve satisfying results and outperform other existing methods under considerations here. The following structure displays the conceptual design of the SRITCSD method for image texture classification. More specifically, it depicts the structure of the training phase and the testing phase for the SRITCSD method. In the training phase, the DWT-FE component denotes the feature-extraction scheme applied for a DWT version image. The feature set, f D W T j , in Eq. (8) is computed via feeding the DWT-FE component with a DWT version image. Let I S V D j represent that image I j is enhanced via the SVD. Another feature set, f S V D , D W T j , in Eq. (11) is calculated via feeding the DWT-FE component with a DWT version of I S V D j . Also, the SVM performs as a multiclassifier with respect to a set of training patterns which are constructed using image texture features, f D W T j and f S V D , D W T j . Meanwhile, the PSO algorithm is employed to optimize the SRITCSD method, which selects the nearly optimal combination of features and a set of parameters utilized in the SVM. In the testing phase of the SRITCSD method, two feature sets, f D W T q and f S V D , D W T q , are computed for a query image I q . The classification result can be obtained via feeding the trained SVM model with f S V D , D W T q to estimate which category the image I q belongs to.Display Omitted The paper presents an image classification technique which extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains.First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image.Also, the SVM serves as a multiclassifier for image texture features.Meanwhile, the particle swarm optimization (PSO) algorithm is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM.The experimental results demonstrate that the method can achieve satisfying results and outperform other existing methods.
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paper_title: Support Vector Method for Multivariate Density Estimation

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A new method for multivariate density estimation is developed based on the Support Vector Method (SVM) solution of inverse ill-posed problems. The solution has the form of a mixture of densities. This method with Gaussian kernels compared favorably to both Parzen's method and the Gaussian Mixture Model method. For synthetic data we achieve more accurate estimates for densities of 2, 6, 12, and 40 dimensions.
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paper_title: Large margin dags for multiclass classification

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We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an N-class problem, the DDAG contains N(N - 1)/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG. The DAGSVM is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.
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paper_title: Support-Vector Networks

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The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. ::: ::: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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paper_title: Long Short-Term Memory

paper_content:

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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Title: Texture Image Analysis and Texture Classification Methods - A Review

Section 1: INTRODUCTION
Description 1: Provide an overview of texture analysis, including the definition of textures, its recognition in human and machine vision, and the main areas of texture analysis.

Section 2: Challenges in Texture Images
Description 2: Discuss the major challenges in texture image analysis such as rotation, noise, scale, viewpoint, and intensity of light.

Section 3: Feature Extraction Method for Categorizing Textures
Description 3: Explain the two-step process of texture classification—feature extraction and classification phase. Include a description of various feature extraction methods.

Section 4: Classification Phase
Description 4: Describe the classification stage including machine learning algorithms used to select the appropriate class for each image.

Section 5: Texture Analysis Application
Description 5: Highlight different practical applications of texture analysis in various domains such as face detection, tracking objects in videos, product quality diagnostics, medical image analysis, remote sensing, and vegetation.

Section 6: Paper Organization
Description 6: Provide an overview of the paper's organization and the main sections to be covered.

Section 7: Texture Classification Methods
Description 7: Discuss the four main categories of texture classification methods—statistical, structural, model-based, and transform methods. Provide details and examples of each method.

Section 8: Combinational State-of-the-Art Texture Analysis Algorithms
Description 8: Review several combined methods for texture analysis that integrate multiple approaches for improved effectiveness.

Section 9: Benchmark Texture Image Datasets
Description 9: Introduce various standard datasets used for evaluating the efficiency of texture classification algorithms.

Section 10: Classification Phase in Texture Classification Methods
Description 10: Provide detailed descriptions of various machine learning classifiers used in the classification phase, including K-Nearest Neighbor, Support Vector Machine, Naive Bayes Classifier, Decision Tree, Artificial Neural Networks, and other classifiers.

Section 11: RESULTS
Description 11: Summarize the properties of texture databases, and methods with their advantages, disadvantages, accuracy, and dataset of each method in tabular form.

Section 12: CONCLUSIONS
Description 12: Conclude the study by summarizing the methods for texture analysis and discussing the main challenges each new method aims to overcome.
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