Kernelized product quantization

doi:10.1016/j.neucom.2016.12.016

	Kernelized product quantization
	Liu, Jie 1,2; Zhang, Yichao 1; Zhou, Jianshe 2; Shi, Jinsheng 2; Zhang, Yongdong 3
	2017-04-26
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	235 页码:15-26
摘要	There has been increasing interest in learning compact binary codes for large-scale image data representation and retrieval. In most existing hashing-based methods, high-dimensional vectors are hashed into Hamming space, and the similarity between two vectors is approximated by the Hamming distance between their binary codes. Although hashing-based binary codes generation methods were widely used, Product Quantization (PQ) has been shown to be more accurate than various hashing-based methods, largely due to its lower quantization distortions and more precise distance computation. However, it is still a challenging problem to generalize PQ to accommodate arbitrary kernels. In this paper, we demonstrate how to employ arbitrary kernel functions in a PQ scheme. First, we propose a Kernelized PQ (KPQ) method based on composite kernels, which serves as a basic framework by making the decomposition of implicit feature space possible. Furthermore, we propose a Kernelized Optimized PQ (KOPQ) method to generalize Optimized Product Quantization (OPQ) to an arbitrary implicit feature space. Finally, we propose a Supervised KPQ (SKPQ) to improve the performance of semantic neighbor search. Both methods are variations of KPQ with the incorporation of their corresponding core techniques, KPCA and KCCA respectively, to the basic KPQ framework. Experiments involving three notable datasets show that KPQ, KOPQ and SKPQ can outperform the state-of-the-art methods for a similarity search in feature space or semantic search.
关键词	High-dimensional similarity search Compact binary coding Product quantization Composite kernel
DOI	10.1016/j.neucom.2016.12.016
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[61525206] ; National Nature Science Foundation of China[61371194] ; National Nature Science Foundation of China[61672361] ; National Nature Science Foundation of China[61428207] ; National High Technology Research and Development Program of China[2014AA015202] ; Beijing Advanced Innovation Center for Imaging Technology[BAICIT-2016009] ; Beijing Natural Science Foundation[4152012]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000395219700003
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7403
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, Jianshe
作者单位	1.Capital Normal Univ, Coll Informat & Engn, Beijing 100048, Peoples R China 2.Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Technol, Beijing 100048, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Jie,Zhang, Yichao,Zhou, Jianshe,et al. Kernelized product quantization[J]. NEUROCOMPUTING,2017,235:15-26.
APA	Liu, Jie,Zhang, Yichao,Zhou, Jianshe,Shi, Jinsheng,&Zhang, Yongdong.(2017).Kernelized product quantization.NEUROCOMPUTING,235,15-26.
MLA	Liu, Jie,et al."Kernelized product quantization".NEUROCOMPUTING 235(2017):15-26.