Probabilistic cost model for nearest neighbor search in image retrieval
Computer Vision and Image Understanding
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Quadra-Embedding: binary code embedding with low quantization error
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Effective hashing for large-scale multimedia search
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
Comparing apples to oranges: a scalable solution with heterogeneous hashing
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
A unified approximate nearest neighbor search scheme by combining data structure and hashing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Many binary code encoding schemes based on hashing have been actively studied recently, since they can provide efficient similarity search, especially nearest neighbor search, and compact data representations suitable for handling large scale image databases in many computer vision problems. Existing hashing techniques encode high-dimensional data points 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. Furthermore, we propose a new binary code distance function, spherical Hamming distance, that is tailored to our hypersphere-based binary coding scheme, and design an efficient iterative optimization process to achieve balanced partitioning of data points for each hash function and independence between hashing functions. Our extensive experiments show that our spherical hashing technique significantly outperforms six state-of-the-art hashing techniques based on hyperplanes across various image benchmarks of sizes ranging from one to 75 million of GIST descriptors. The performance gains are consistent and large, up to 100% improvements. The excellent results confirm the unique merits of the proposed idea in using hyperspheres to encode proximity regions in high-dimensional spaces. Finally, our method is intuitive and easy to implement.