HYPERSPHERICAL EMBEDDING ITERATIVE QUANTIZATION HASHING

The iterative quantization hashing learns similaritypreserving binary codes by rotating the data points in a space projected by the principal components of data to minimize the quantization error between hash function outputs and corresponding binary codes.However, in some cases, a rotation may not be enough to preserve similarity by Hamming distance. In this paper, we propose a hashing method that embeds the data pointsonto the surface of a hypersphere according to the data similaritybefore performingthe iterative quantization. Experimental results show that the hash codes learned by the proposed method approximate theneighborhood relationship with higher precision.