World of color texture: unified solution for enhancement, invariance and recognition
Authors: Umasankar Kandaswamy
Venue:  (2008)

Most real world computer vision based applications have minimal or no control over the image acquisition environment. Algorithms that are developed in lab environment using ideal data often fail to perform with the same accuracy when tested under non-ideal circumstances. Pattern recognition and image registration applications often suffer the most in such real world conditions since such algorithms use color and textural information that are prone to perturbations caused by changes in image acquisition conditions. Even though several image enhancement techniques have been proposed in the past to combat this problem, recent independent studies done using relatively small datasets show only a few algorithms perform with acceptable performance and even less number of those algorithms are truly device independent. In this work, we study the effects of changes in illumination and viewing conditions on the classification performance of different texture analysis algorithms in a very large color texture dataset and address the problem they encounter systematically at different stages of pattern recognition, namely pre-processing, feature extraction and post-processing. The first major contribution of this work is benchmarking state-of-the-art algorithms in one of the largest non-ideal color texture dataset. Two facets of this survey are: organization of one of the largest color texture datasets and its associated bench-marking. Even though color and texture have been studied for decades, the number of classes used in an average color texture dataset has always been less than 100. Collection of more than 290 different classes presented under more than 25 different viewing conditions were used for analyzing the performance of eight different state-of-the-art algorithms, namely, Gabor Filters, Grayscale Co-Occurrence Matrix, Local Binary Pattern, Local Binary Pattern-rotation invariant, Multiresolution Histograms, Leung-Malik&sacute; 3D-Textons and MR8. The comparative results of these algorithms under such extreme non-ideal conditions provided deeper insights on how the number of classes affects the intra-class and interclass feature spaces of different algorithms. The survey clearly show the need for efficient and effective algorithm under such extreme conditions. The next two core contributions of this work, namely, Minvariance Transformation (MVT), and Collective Color Texture Model (CCTM), are related to the problem of obtaining invariant representation and extracting textural feature of an objects surface. While the former addresses the inherent problems in existing color texture enhancement techniques, such as grayworld normalization, retinex theory and channel-wise histogram equalization, later addresses the fundamental problem in texture models. Existing texture models like Gibbs random field model, Gaussian/Markov random field models and the famous Wold texture model fail to take the interdependencies between color and texture into account. Our proposed CCTM minimizes the effect of illumination related perturbations in color and texture of the color texture by taking the changes in edge and non-edge pixels on each channel into account. Experiments conducted using difficult non-deal color texture datastes showed that texton based features extracted using CCTM had not only the best performance compared to existing techniques but also the most reliable results under different image acquisition circumstances. While the last two contributions address the illumination related image perturbations, the third contribution, Error-Encoded PDE-textons (EEPDE), focuses on the post-processing. Eliminating erroneous information that are not accounted at the stages of pre-processing and feature extraction is a vital process that could significantly improve the performance of the pattern recognition system. The proposed post-processing algorithm EEPDE-textons, minimizes the effect of features from regions that are extremely perturbed by assigning proportionally lower weights and thereby minimizing the contribution of those features on the distance measure. When tested for non-ideal face recognition application EEPDE-textons performed superior than three other state-of-the-art algorithms even in the presence of illumination variation, occlusion and expression. Some of the noteworthy quantitative results of this work are: (1) Pre-Processing. Using color features, Minvariance Transformed images out-performed both grayworld normalization and channel-wise equalization by an average of 4%. While using textural features, Minvarinace Transformation out-performed grayworld normalization, channel-wise equalization, and Retinex algorithm by an average of 7%. (2) Feature Extraction. Even in the presence of extreme changes in viewing conditions the proposed Collective Color Texture Model, when used under the principle of discriminative modelling, outperformed other popular state-of-the-art texton-based algorithms by an average of 7% in texture classification and 5% 3D texture retrieval. (3) Computational Cost: Compared to other popular texton-based algorithms, Collective Color Texture Model based textons are at least 2.5 Limes more efficient while achieving improved robustness. (4) Post-Processing: When tested for non-ideal face recognition application Error Encoded PDE-textons performed superior than three other state-of-the-art algorithms by an average of 9% Equal Error Rate even in the presence of illumination variation, occlusion and expression. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player