Pairwise Rotation Invariant Co-occurrence Local Binary Pattern
文献类型：期刊论文
| 作者 | Xianbiao Qi; Rong Xiao; Chun-Guang Li; Yu Qiao; Jun Guo; Xiaoou Tang |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2014 |
| 英文摘要 | 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. |
| 收录类别 | SCI |
| 原文出处 | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6787082 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/5338]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
| 推荐引用方式 GB/T 7714 | Xianbiao Qi,Rong Xiao,Chun-Guang Li,et al. Pairwise Rotation Invariant Co-occurrence Local Binary Pattern[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2014. |
| APA | Xianbiao Qi,Rong Xiao,Chun-Guang Li,Yu Qiao,Jun Guo,&Xiaoou Tang.(2014).Pairwise Rotation Invariant Co-occurrence Local Binary Pattern.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. |
| MLA | Xianbiao Qi,et al."Pairwise Rotation Invariant Co-occurrence Local Binary Pattern".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2014). |
入库方式： OAI收割
来源：深圳先进技术研究院
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