Kernelized product quantization

doi:10.1016/j.neucom.2016.12.016

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Kernelized product quantization
	Liu, Jie 1,2; Zhang, Yichao 2; Zhou, Jianshe 1; Shi, Jinsheng 1; Zhang, Yongdong 3
刊名	NEUROCOMPUTING
	2017-04-26
卷号	235 页码:15-26
关键词	High-dimensional similarity search Compact binary coding Product quantization Composite kernel
ISSN号	0925-2312
DOI	10.1016/j.neucom.2016.12.016
英文摘要	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.
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
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000395219700003
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/7402]
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, Beijing Adv Innovat Ctr Imaging Technol, Beijing 100048, Peoples R China 2.Capital Normal Univ, Coll Informat & Engn, 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.