CBIR algorithm based on relevance feedback and robust local binary patterns

被引：0
|
作者：
Sun T. [1 ,2 ]
Geng G.-H. [2 ]
机构：
[1] College of Network Engineering, Zhoukou Normal University, Zhoukou, 466001, Henan
[2] Visualization Institute, Northwestern University, Xi'an
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2016年 / 39卷 / 05期
关键词：
Bobust local binary pattern; Feature extraction; Image retrieval; Relevance feedback;
D O I：
10.13190/j.jbupt.2016.05.004
中图分类号：
学科分类号：
摘要：
A algorithm of content based image retrieval (CBIR) based on robust local binary patterns (RLBP) and relevance feedback (RF) was proposed. RLBP is a kind of feature extraction operator with good performance, which has strong robustness to the noise and illumination changes. The original data will not be changed with RLBP, and the accuracy of feature extraction can be improved. RF enables the system to learn the user's preferences and guide the search results. Experiments with several texture databases show that the accuracy and robustness of the proposed algorithm is better than that of the similar algorithms. © 2016, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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收藏
页码：16 / 19
页数：3
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