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Design of Instruction Analyzer with Semantic-Based Loop Unrolling Mechanism in the Hyperscalar Architecture Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Supervised Representation Hash Codes Learning

Authors : Huei-Fang Yang, Cheng-Hao Tu, Chu-Song Chen

Published in: New Trends in Computer Technologies and Applications

Publisher: Springer Singapore

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Abstract

Learning-based hashing has been widely employed for large-scale similarity retrieval due to its efficient computation and compressed storage. In this paper, we propose ResHash, a deep representation hash code learning approach to learning compact and discriminative binary codes. In ResHash, we assume that each semantic label has its own representation codeword and these codewords guide hash coding. The codewords are attractors that attract semantically similar images and are also repulsors that repel semantically dissimilar ones. Furthermore, ResHash jointly learns compact binary codes and discover representation codewords from data by a simple margin ranking loss, making it easily realizable and avoiding the need to hand-craft the codewords beforehand. Experimental results on standard benchmark datasets show the effectiveness of ResHash.

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Metadata
Title
Supervised Representation Hash Codes Learning
Authors
Huei-Fang Yang
Cheng-Hao Tu
Chu-Song Chen
Copyright Year
2019
Publisher
Springer Singapore
Book
New Trends in Computer Technologies and Applications

Print ISBN: 978-981-13-9189-7

Electronic ISBN: 978-981-13-9190-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-9190-3_13

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