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

• Published in: International journal of multimedia information retrieval 2015-09, Vol.4 (3), p.205-216
• Main Authors: Kamel, Abdelrahman, Mahdy, Youssef B., Hussain, Khaled F.
• Format: Article
• Language: English
• Subjects: Algorithms; Benchmarks; Bins; Codes; Computer Science; Data Mining and Knowledge Discovery; Database Management; Image Processing and Computer Vision; Image retrieval; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Methods; Multimedia Information Systems; Regular Paper; Retrieval; Search process
• ISSN: 2192-6611; 2192-662X
• DOI: 10.1007/s13735-014-0061-0

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.