Multi-Bin search: improved large-scale content-based image retrieval

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• 作者：Abdelrahman Kamel ; Youssef B. Mahdy…
• 关键词：Content ; based image retrieval ; Multi ; Bin search ; Binary descriptors ; Binary hashing
• 刊名：International Journal of Multimedia Information Retrieval
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：4
• 期：3
• 页码：205-216
• 全文大小：1,542 KB
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• 作者单位：Abdelrahman Kamel (1)
  Youssef B. Mahdy (1)
  Khaled F. Hussain (1)
  
  1. Computer Science Department, Faculty of Computers and Information, Assiut University, Assiut, 71516, Egypt
  
• 刊物主题：Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl. Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general;
• 出版者：Springer London
• ISSN：2192-662X

文摘

The challenge of large-scale content-based image retrieval (CBIR) has been recently addressed by many promising approaches. In this work, a new approach that jointly optimizes the search precision and time for large-scale CBIR is presented. This is achieved using binary local image descriptors, such as BRIEF or BRISK, along with binary hashing methods, such as Locality-Sensitive Hashing and Spherical Hashing (SH). The proposed approach, named Multi-Bin Search, improves the retrieval precision of binary hashing methods through computing, storing and indexing the nearest neighbor bins for each bin generated from a binary hashing method. Then, the search process does not only search the targeted bin, but also it searches the nearest neighbor bins. To efficiently search inside targeted bins, a fast exhaustive-search equivalent algorithm, inspired by Norm Ordered Matching, has been used. Also, a result reranking step that increases the retrieval precision is introduced, but with a slight increase in search time. Experimental evaluations over famous benchmarking datasets (such as the University of Kentucky Benchmarking, the INRIA Holidays, and the MIRFLICKR-1M) show that the proposed approach highly improves the retrieval precision of the state-of-art binary hashing methods.