Heterogeneous Metric Learning for Cross-Modal Multimedia Retrieval

ISCAS OpenIR

	Heterogeneous Metric Learning for Cross-Modal Multimedia Retrieval
	Deng, Jun; Du, Liang; Shen, Yi-Dong
	2013
会议名称	14th International Conference on Web Information Systems Engineering (WISE)
页码	43-56
会议日期	OCT 13-15, 2013
会议地点	Nanjing, PEOPLES R CHINA
收录类别	CPCI
出版地	SPRINGER-VERLAG BERLIN
ISSN	0302-9743
ISBN	978-3-642-41230-1; 978-3-642-41229-5
部门归属	[Deng, Jun; Du, Liang; Shen, Yi-Dong] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China.
摘要	Due to the massive explosion of multimedia content on the web, users demand a new type of information retrieval, called cross-modal multimedia retrieval where users submit queries of one media type and get results of various other media types. Performing effective retrieval of heterogeneous multimedia content brings new challenges. One essential aspect of these challenges is to learn a heterogeneous metric between different types of multimedia objects. In this paper, we propose a Bayesian personalized ranking based heterogeneous metric learning (BPRHML) algorithm, which optimizes for correctly ranking the retrieval results. It uses pairwise preference constraints as training data and explicitly optimizes for preserving these constraints. To further encouraging the smoothness of learning results, we integrate graph regularization with Bayesian personalized ranking. The experimental results on two publicly available datasets show the effectiveness of our method.; Due to the massive explosion of multimedia content on the web, users demand a new type of information retrieval, called cross-modal multimedia retrieval where users submit queries of one media type and get results of various other media types. Performing effective retrieval of heterogeneous multimedia content brings new challenges. One essential aspect of these challenges is to learn a heterogeneous metric between different types of multimedia objects. In this paper, we propose a Bayesian personalized ranking based heterogeneous metric learning (BPRHML) algorithm, which optimizes for correctly ranking the retrieval results. It uses pairwise preference constraints as training data and explicitly optimizes for preserving these constraints. To further encouraging the smoothness of learning results, we integrate graph regularization with Bayesian personalized ranking. The experimental results on two publicly available datasets show the effectiveness of our method.
关键词	Metric Learning Heterogeneous Spaces Multimedia
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16527
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Deng, Jun,Du, Liang,Shen, Yi-Dong. Heterogeneous Metric Learning for Cross-Modal Multimedia Retrieval[C]. SPRINGER-VERLAG BERLIN,2013:43-56.