Two decades of local binary patterns: A survey
---
paper_title: Face Description with Local Binary Patterns: Application to Face Recognition

paper_content:

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed
---
paper_title: Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

paper_content:

Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation
---
paper_title: Local Binary Patterns: New Variants and Applications

paper_content:

This book introduces Local Binary Patterns (LBP), arguably one of the most powerful texture descriptors, and LBP variants. This volume provides the latest reviews of the literature and a presentation of some of the best LBP variants by researchers at the forefront of textual analysis research and research on LBP descriptors and variants. The value of LBP variants is illustrated with reported experiments using many databases representing a diversity of computer vision applications in medicine, biometrics, and other areas. There is also a chapter that provides an excellent theoretical foundation for texture analysis and LBP in particular. A special section focuses on LBP and LBP variants in the area of face recognition, including thermal face recognition. This book will be of value to anyone already in the field as well as to those interested in learning more about this powerful family of texture descriptors.
---
paper_title: Texture discrimination with multidimensional distributions of signed gray level differences

paper_content:

The statistics of gray-level di!erences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray-level di!erences and their multidimensional distributions for texture description. The present approach has important advantages compared to earlier related approaches based on gray level cooccurrence matrices or histograms of absolute gray-level di!erences. Experiments with di$cult texture classi"cation and supervised texture segmentation problems show that our approach provides a very good and robust performance in comparison with the mainstream paradigms such as cooccurrence matrices, Gaussian Markov random "elds, or Gabor "ltering. ( 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
---
paper_title: Face Description with Local Binary Patterns: Application to Face Recognition

paper_content:

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed
---
paper_title: Monogenic-LBP: A new approach for rotation invariant texture classification

paper_content:

Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of rotation invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type computed by the 1st-order and 2ndorder Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated.
---
paper_title: A comparative study of texture measures with classification based on featured distributions

paper_content:

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
---
paper_title: Retrieval of translated, rotated and scaled color textures

paper_content:

A new method for color texture retrieval using color and edge features is proposed in this study. The proposed method unifies color and edge features rather than simply analyzing only color characteristics. First, the distributions of color and local edge patterns are used to derive a similarity measure for a pair of textures. Then, a retrieval method based on the similarity measure is proposed to retrieve texture images from a database of color textures. Finally, the similarity measure is extended to retrieve texture regions from a database of natural images. Since the proposed feature distributions can resist variations in translation, rotation and scale, our method has the ability to retrieve texture images or regions that change in translation, rotation and/or scale. The effectiveness and practicability of the proposed method have been demonstrated by various experiments.
---
paper_title: Constructing Local Binary Pattern Statistics by Soft Voting

paper_content:

In this paper we propose a novel method for constructing Local Binary Pattern (LBP) statistics for image appearance description. The method is inspired by the kernel density estimation designed for estimating the underlying probability function of a random variable. An essential part of the proposed method is the use of Hamming distance. Compared to the standard LBP histogram statistics where one labeled pixel always contributes to one bin of the histogram, the proposed method exploits a kernel-like similarity function to determine weighted votes contributing several possible pattern types in the statistic. As a result, the method yields a more reliable estimate of the underlying LBP distribution of the given image. In overall, the method is easy to implement and outperforms the standard LBP histogram description in texture classification and in biometrics-related face verification. We demonstrate that the method is extremely potential in problems where the number of pixels is limited. This makes the method very promising, for example, in low-resolution image description and the description of interest regions. Another interesting property of the proposed method is that it can be easily integrated with many existing LBP variants that use label statistics as descriptors.
---
paper_title: FLBP: Fuzzy Local Binary Patterns

paper_content:

In this chapter, a variant of the Local Binary Patterns method that extends the ability of the method to cope with noisy texture by utilizing fuzzy modelling techniques is presented. The presented generalised Fuzzy Binary Patterns model is applied to the classic Local Binary Patterns method as well as to the Local Binary Patterns with Contrast measure method, resulting to the respective fuzzy logic based methods. Supervised classification experiments were conducted on a wide range of natural and medical texture images, degraded by different types and intensities of additive noise, in order to evaluate the efficiency of the Fuzzy Local Binary Patterns method and its fusion with other proposed methods. Fuzzy Binary Patterns based methods outperform the respective methods based on the classic Binary Patterns model for all types of images and noise, indicating the efficiency of fuzzy modelling in coping with the uncertainty introduced to texture due to noise.
---
paper_title: Classification with color and texture: jointly or separately?

paper_content:

Abstract Current approaches to color texture analysis can be roughly divided into two categories: methods that process color and texture information separately, and those that consider color and texture a joint phenomenon. In this paper, both approaches are empirically evaluated with a large set of natural color textures. The classification performance of color indexing methods is compared to gray-scale and color texture methods, and to combined color and texture methods, in static and varying illumination conditions. Based on the results, we argue that color and texture are separate phenomena that can, or even should, be treated individually.
---
paper_title: CENTRIST: A Visual Descriptor for Scene Categorization

paper_content:

CENsus TRansform hISTogram (CENTRIST), a new visual descriptor for recognizing topological places or scene categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its visual descriptor to possess properties that are different from other vision domains (e.g., object recognition). CENTRIST satisfies these properties and suits the place and scene recognition task. It is a holistic representation and has strong generalizability for category recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. Our experiments demonstrate that CENTRIST outperforms the current state of the art in several place and scene recognition data sets, compared with other descriptors such as SIFT and Gist. Besides, it is easy to implement and evaluates extremely fast.
---
paper_title: Learning binarized pixel-difference pattern for scene recognition

paper_content:

Local binary pattern (LBP) and its variants have been used in scene recognition. However, most existing approaches rely on a pre-defined LBP structure to extract features. Those pre-defined structures can be generalized as the patterns constructed from the binarized pixel differences in a local neighborhood. Instead of using a handcraft structure, we propose to learn binarized pixel-difference patterns (BPP). We cast the problem as a feature selection problem and solve it by an incremental search via the criterion of minimum-redundancy-maximum-relevance. Then, BPP features are extracted based on the structures derived. On two challenging scene recognition databases, the proposed approach significantly outperforms the state of the arts.
---
paper_title: A comprehensive benchmark of local binary pattern algorithms for texture retrieval

paper_content:

Image retrieval is a well researched area and often based on integrating various kinds of image features. Apart from colour features, texture features are deemed crucial for successful image retrieval. Local binary pattern (LBP) based texture algorithms have gained significant popularity in recent years and have been shown to be useful for a variety of tasks. In this paper, we provide a comprehensive benchmark of LBP based methods for texture retrieval. In particular, a comparison of 16 LBP variants leading to 38 different texture descriptors, are evaluated on a large dataset of more than 6000 texture images. Interestingly, conventional LBP features are shown to work best, while almost all LBP methods are shown to significantly outperform other texture methods including Tamura, co-occurrence and Gabor features.
---

Title: Two Decades of Local Binary Patterns: A Survey

Section 1: An overview of basic LBP operators
Description 1: Summarize the fundamental concepts, theoretical bases, and initial implementations of the Local Binary Patterns (LBP) operator.

Section 2: Different types of variants
Description 2: Discuss various extensions and modifications of the original LBP to enhance its performance and robustness across different applications.

Section 3: Preprocessing
Description 3: Detail preprocessing techniques used prior to LBP computation, including methods like Gabor filtering and edge detection to improve performance under various conditions.

Section 4: Neighborhood topology and sampling
Description 4: Explain the flexibility of LBP in terms of neighborhood topology and sampling, and how different shapes and configurations can be used based on the application requirements.

Section 5: Thresholding and encoding
Description 5: Explore different thresholding and encoding techniques that address the limitations of the original LBP, enhancing robustness to noise and gray level changes.

Section 6: Considering co-occurrences
Description 6: Describe approaches that incorporate co-occurrences of LBP codes to improve detection performance and feature representation.

Section 7: Feature selection and learning
Description 7: Highlight methods for reducing the dimensionality of LBP features and selecting the most discriminative patterns through feature selection and machine learning techniques.

Section 8: Other methods inspired by LBP
Description 8: Summarize new local image descriptors developed based on LBP principles, such as Local Phase Quantization (LPQ) and Patterns of Oriented Edge Magnitudes (POEM).

Section 9: Variants of spatiotemporal LBP
Description 9: Discuss the adaptations of LBP for spatiotemporal analysis, extending its applicability from static images to dynamic video sequences.

Section 10: Analysis of depth and 4D images
Description 10: Detail the use of LBP variants for analyzing depth (3D) and 4D images, with applications in fields like medical imaging and gesture recognition.

Section 11: Conclusions
Description 11: Summarize the overall advantages of LBP, highlight key variants and their contributions, and discuss future challenges and directions for LBP research.