Supervised Quantization For Similarity Search

Abstract

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.

Publication Date

12-9-2016

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volume

2016-December

Number of Pages

2018-2026

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2016.222

Socpus ID

84986268753 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84986268753

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