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

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Abstract

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.

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Acknowledgments

Thanks to the Multimedia Lab at the Multimedia Department, Faculty of Computers and Information, Assiut University, for using the computers of the lab to carry out the experiments presented in this work.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Computers and Information, Assiut University, Assiut, 71516, Egypt

    Abdelrahman Kamel, Youssef B. Mahdy & Khaled F. Hussain

Authors
  1. Abdelrahman Kamel
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  2. Youssef B. Mahdy
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  3. Khaled F. Hussain
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Corresponding author

Correspondence to Abdelrahman Kamel.

Additional information

Part of this work was published in the proceedings of the 2013 IEEE 20th International Conference on Image Processing (ICIP) [17]. Source code of the proposed work can be downloaded from [11].

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Kamel, A., Mahdy, Y.B. & Hussain, K.F. Multi-Bin search: improved large-scale content-based image retrieval. Int J Multimed Info Retr 4, 205–216 (2015). https://doi.org/10.1007/s13735-014-0061-0

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  • DOI: https://doi.org/10.1007/s13735-014-0061-0

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