  Hashing methods, which encode high-dimensional images with compact discrete
codes, have been widely applied to enhance large-scale image retrieval. In this
paper, we put forward Deep Spherical Quantization (DSQ), a novel method to make
deep convolutional neural networks generate supervised and compact binary codes
for efficient image search. Our approach simultaneously learns a mapping that
transforms the input images into a low-dimensional discriminative space, and
quantizes the transformed data points using multi-codebook quantization. To
eliminate the negative effect of norm variance on codebook learning, we force
the network to L_2 normalize the extracted features and then quantize the
resulting vectors using a new supervised quantization technique specifically
designed for points lying on a unit hypersphere. Furthermore, we introduce an
easy-to-implement extension of our quantization technique that enforces
sparsity on the codebooks. Extensive experiments demonstrate that DSQ and its
sparse variant can generate semantically separable compact binary codes
outperforming many state-of-the-art image retrieval methods on three
benchmarks.
