  In this paper, we address the problem of searching for semantically similar
images from a large database. We present a compact coding approach, supervised
quantization. Our approach simultaneously learns feature selection that
linearly transforms the database points into a low-dimensional discriminative
subspace, and quantizes the data points in the transformed space. The
optimization criterion is that the quantized points not only approximate the
transformed points accurately, but also are semantically separable: the points
belonging to a class lie in a cluster that is not overlapped with other
clusters corresponding to other classes, which is formulated as a
classification problem. The experiments on several standard datasets show the
superiority of our approach over the state-of-the art supervised hashing and
unsupervised quantization algorithms.
