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
Copyright Status
Unknown
Socpus ID
84986268753 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84986268753
STARS Citation
Wang, Xiaojuan; Zhang, Ting; Qi, Guo Jun; Tang, Jinhui; and Wang, Jingdong, "Supervised Quantization For Similarity Search" (2016). Scopus Export 2015-2019. 4322.
https://stars.library.ucf.edu/scopus2015/4322