Journal ArticleDOI
A Survey on Learning to Hash
TLDR
In this paper, a comprehensive survey of the learning to hash algorithms is presented, categorizing them according to the manners of preserving the similarities into: pairwise similarity preserving, multi-wise similarity preservation, implicit similarity preserving and quantization, and discuss their relations.Abstract:
Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.read more
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Book ChapterDOI
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
TL;DR: LQ-Nets as mentioned in this paper proposes to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization.
Journal ArticleDOI
SIFT Meets CNN: A Decade Survey of Instance Retrieval
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Sketch-based Manga Retrieval using Manga109 Dataset
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SIFT Meets CNN: A Decade Survey of Instance Retrieval
Liang Zheng,Yi Yang,Qi Tian +2 more
TL;DR: A comprehensive survey of instance retrieval over the last decade is presented in this paper, where two broad categories, SIFT-based and CNN-based methods, are presented, according to the codebook size, and the literature is organized into using large/medium-sized/small codebooks.
References
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Proceedings ArticleDOI
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