DocumentCode :
75949
Title :
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern
Author :
Xianbiao Qi ; Rong Xiao ; Chun-Guang Li ; Yu Qiao ; Jun Guo ; Xiaoou Tang
Author_Institution :
Sch. of Inf. & Commun. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China
Volume :
36
Issue :
11
fYear :
2014
fDate :
Nov. 1 2014
Firstpage :
2199
Lastpage :
2213
Abstract :
Designing effective features is a fundamental problem in computer vision. However, it is usually difficult to achieve a great tradeoff between discriminative power and robustness. Previous works shown that spatial co-occurrence can boost the discriminative power of features. However the current existing co-occurrence features are taking few considerations to the robustness and hence suffering from sensitivity to geometric and photometric variations. In this work, we study the Transform Invariance (TI) of co-occurrence features. Concretely we formally introduce a Pairwise Transform Invariance (PTI) principle, and then propose a novel Pairwise Rotation Invariant Co-occurrence Local Binary Pattern (PRICoLBP) feature, and further extend it to incorporate multi-scale, multi-orientation, and multi-channel information. Different from other LBP variants, PRICoLBP can not only capture the spatial context co-occurrence information effectively, but also possess rotation invariance. We evaluate PRICoLBP comprehensively on nine benchmark data sets from five different perspectives, e.g., encoding strategy, rotation invariance, the number of templates, speed, and discriminative power compared to other LBP variants. Furthermore we apply PRICoLBP to six different but related applications-texture, material, flower, leaf, food, and scene classification, and demonstrate that PRICoLBP is efficient, effective, and of a well-balanced tradeoff between the discriminative power and robustness.
Keywords :
computer vision; feature extraction; transforms; PRICoLBP feature; PTI principle; computer vision; pairwise rotation invariant co-occurrence local binary pattern; pairwise transform invariance principle; spatial co-occurrence; Encoding; Feature extraction; Histograms; Image color analysis; Lighting; Robustness; Transforms; Co-occurrence LBPs; flower recognition; food recognition; leaf recognition; material recognition; rotation invariance; scene recognition; texture classification;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2014.2316826
Filename :
6787082
Link To Document :
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