Learning Multifunctional Binary Codes for Personalized Image Retrieval

doi:10.1007/s11263-020-01315-0

	Learning Multifunctional Binary Codes for Personalized Image Retrieval
	Liu, Haomiao 1,2,3; Wang, Ruiping 1,2; Shan, Shiguang 1,2; Chen, Xilin 1,2
	2020-03-17
发表期刊	INTERNATIONAL JOURNAL OF COMPUTER VISION
ISSN	0920-5691
页码	20
摘要	Due to the highly complex semantic information of images, even with the same query image, the expected content-based image retrieval results could be very different and personalized in different scenarios. However, most existing hashing methods only preserve one single type of semantic similarity, making them incapable of addressing such realistic retrieval tasks. To deal with this problem, we propose a unified hashing framework to encode multiple types of information into the binary codes by exploiting convolutional networks (CNNs). Specifically, we assume that typical retrieval tasks are generally defined in two aspects, i.e. high-level semantics (e.g. object categories) and visual attributes (e.g. object shape and color). To this end, our Dual Purpose Hashing model is trained to jointly preserve two kinds of similarities characterizing the two aspects respectively. Moreover, since images with both category and attribute labels are scarce, our model is carefully designed to leverage the abundant partially labelled data as training inputs to alleviate the risk of overfitting. With such a framework, the binary codes of new-coming images can be readily obtained by quantizing the outputs of a specific CNN layer, and different retrieval tasks can be achieved by using the binary codes in different ways. Experiments on two large-scale datasets show that our method achieves comparable or even better performance than those state-of-the-art methods specifically designed for each individual retrieval task while being more compact than the compared methods.
关键词	Image retrieval Multi-task learning Hashing
DOI	10.1007/s11263-020-01315-0
收录类别	SCI
语种	英语
资助项目	973 Program[2015CB351802] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61772500] ; CAS[QYZDJ-SSWJSC009] ; Youth Innovation Promotion Association[2015085]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000520662000005
出版者	SPRINGER
引用统计	被引频次：2[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/2XEOYT63/14089
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Ruiping
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Huawei EI Innovat Lab, Beijing 100085, Peoples R China
推荐引用方式 GB/T 7714	Liu, Haomiao,Wang, Ruiping,Shan, Shiguang,et al. Learning Multifunctional Binary Codes for Personalized Image Retrieval[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2020:20.
APA	Liu, Haomiao,Wang, Ruiping,Shan, Shiguang,&Chen, Xilin.(2020).Learning Multifunctional Binary Codes for Personalized Image Retrieval.INTERNATIONAL JOURNAL OF COMPUTER VISION,20.
MLA	Liu, Haomiao,et al."Learning Multifunctional Binary Codes for Personalized Image Retrieval".INTERNATIONAL JOURNAL OF COMPUTER VISION (2020):20.