-
Discriminative Quantization for Fast Similarity Search
AbstractRecent decade has witnessed a growing surge of research on encoding high-dimensional objects with compact discrete codes. In this paper, we present a new supervised quantization technique to learn discriminative and compact codes for large scale retrieval tasks. To achieve fast and accurate search, the proposed algorithm learns a discriminative embedding of the input points and at the same time encodes the embedded points with compact codes to reduce storage cost.
Related Material
[pdf][bibtex]@InProceedings{Eghbali_2019_CVPR_Workshops,
author = {Eghbali, Sepehr and Tahvildari, Ladan},
title = {Discriminative Quantization for Fast Similarity Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
These CVPR 2019 workshop papers are the Open Access versions, provided by the Computer Vision Foundation.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
This material is presented to ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by other copyright holders.
All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.