Spherical Hashing: Binary Code Embedding with Hyperspheres

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Many binary code embedding schemes have been actively studied recently, since they can provide efficient similarity search, and compact data representations suitable for handling large scale image databases. Existing binary code embedding techniques encode high-dimensional data by using hyperplane-based hashing functions. In this paper we propose a novel hypersphere-based hashing function, spherical hashing, to map more spatially coherent data points into a binary code compared to hyperplane-based hashing functions. We also propose a new binary code distance function, spherical Hamming distance, tailored for our hypersphere-based binary coding scheme, and design an efficient iterative optimization process to achieve both balanced partitioning for each hash function and independence between hashing functions. Furthermore, we generalize spherical hashing to support various similarity measures defined by kernel functions. Our extensive experiments show that our spherical hashing technique significantly outperforms state-of-the-art techniques based on hyperplanes across various benchmarks with sizes ranging from one to 75 million of GIST, BoW and VLAD descriptors. The performance gains are consistent and large, up to 100 percent improvements over the second best method among tested methods. These results confirm the unique merits of using hyperspheres to encode proximity regions in high-dimensional spaces. Finally, our method is intuitive and easy to implement.
Publisher
IEEE COMPUTER SOC
Issue Date
2015-11
Language
English
Article Type
Article
Keywords

NEAREST-NEIGHBOR SEARCH; CLASSIFICATION; FEATURES; KERNELS; SPACES

Citation

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.37, no.11, pp.2304 - 2316

ISSN
0162-8828
DOI
10.1109/TPAMI.2015.2408363
URI
http://hdl.handle.net/10203/205217
Appears in Collection
CS-Journal Papers(저널논문)
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