Supervised Quantization for Similarity Search
This addresses the need for efficient and accurate similarity search in image databases, representing an incremental improvement over existing methods.
The paper tackles the problem of searching for semantically similar images from large databases by introducing supervised quantization, which learns feature selection and quantization simultaneously to improve accuracy and separability. Experiments on standard datasets demonstrate superiority over state-of-the-art supervised hashing and unsupervised quantization algorithms.
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.