Published on Tue Mar 13 2018

A Review on Image Texture Analysis Methods

Shervan Fekri-Ershad

texture classification is an active topic in image processing. There are many approaches to extracting texture features in gray-level images. The texture analysis methods can be categorized in 4 groups.

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Abstract

Texture classification is an active topic in image processing which plays an important role in many applications such as image retrieval, inspection systems, face recognition, medical image processing, etc. There are many approaches extracting texture features in gray-level images such as local binary patterns, gray level co-occurrence matrices, statistical features, skeleton, scale invariant feature transform, etc. The texture analysis methods can be categorized in 4 groups titles: statistical methods, structural methods, filter-based and model based approaches. In many related researches, authors have tried to extract color and texture features jointly. In this respect, combined methods are considered as efficient image analysis descriptors. Mostly important challenges in image texture analysis are rotation sensitivity, gray scale variations, noise sensitivity, illumination and brightness conditions, etc. In this paper, we review most efficient and state-of-the-art image texture analysis methods. Also, some texture classification approaches are survived.

Sat Apr 13 2019
Computer Vision
Texture image analysis and texture classification methods - A review
texture is the main term used to define objects or concepts of a given image. Texture analysis plays an important role in computer vision cases such as object recognition, surface defect detection, pattern recognition, medical image analysis, etc. Since now many approaches have been proposed to describe texture images accurately.
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Thu Nov 10 2011
Computer Vision
A Novel Approach to Texture classification using statistical feature
texture is an important spatial feature which plays a vital role in content-based image retrieval. The enormous growth of the internet and the wide use of digital data have increased the need for efficient image database creation and retrieval procedure.
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Thu Dec 23 2010
Computer Vision
Texture feature extraction in the spatial-frequency domain for content-based image retrieval
Image-retrieval based on texture features is receiving special attention because of the omnipresence of this visual feature in most real-world images. This paper highlights the state-of-the-art and current progress relevant to texture-based image retrieval and spatial-frequency image representations.
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Mon Mar 12 2018
Computer Vision
Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering
texture analysis and classification are some of the problems which have been paying much attention by image processing scientists since late 80s. If texture analysis is done accurately, it can be used in many cases such as object tracking, visual pattern recognition, and face recognition. The proposed approach is included two stages.
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Wed Nov 30 2011
Computer Vision
Invariant texture analysis through Local Binary Patterns
In many image processing applications, such as segmentation and classification, the selection of robust features descriptors is crucial to improve discrimination capabilities. In the past few years the local binary pattern(LBP) approach has gained increased acceptance due to its computational simplicity.
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Tue Dec 29 2015
Computer Vision
Combined statistical and model based texture features for improved image classification
This paper aims to improve the accuracy of texture classification based on five different texture methods. Threeistical-based and two model-based methods are used to extract texture features from eight different texture images. Their accuracy is ranked after using each method individually and in pairs.
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