University of Oulu

Li Liu, Paul Fieguth, Yulan Guo, Xiaogang Wang, Matti Pietikäinen. Local binary features for texture classification: Taxonomy and experimental study. Pattern Recognition, Volume 62, 2017, Pages 135-160, ISSN 0031-3203.

Local binary features for texture classification : taxonomy and experimental study

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Author: Liu, Li1; Fieguth, Paul2; Guo, Yulan3;
Organizations: 1College of Information System and Management, National University of Defense Technology, 109 Deya Road, Changsha, Hunan 410073, China
2Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
3College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China
4Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
5The Center for Machine Vision Research, University of Oulu, Oulu 90014, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.9 MB)
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Language: English
Published: Elsevier, 2017
Publish Date: 2019-02-25


Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. Truly a large number of LBP variants has been proposed, to the point that it can become overwhelming to grasp their respective strengths and weaknesses, and there is a need for a comprehensive study regarding the prominent LBP-related strategies. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.

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Series: Pattern recognition
ISSN: 0031-3203
ISSN-E: 1873-5142
ISSN-L: 0031-3203
Volume: 62
Pages: 135 - 160
DOI: 10.1016/j.patcog.2016.08.032
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
222 Other engineering and technologies
Funding: This work has been supported by the National Natural Science Foundation of China under contract numbers 61202336, 61602499 and 61601481 and by the Open Project Program of the National Laboratory of Pattern Recognition (201407354).
Copyright information: © 2016 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license