Sparse semantic metric learning for image retrieval

	Sparse semantic metric learning for image retrieval
	Liu, Jing; Li, Zechao; Lu, Hanqing
发表期刊	MULTIMEDIA SYSTEMS
	2014-11-01
卷号	20 期号:6 页码:635-643
文章类型	Article
摘要	Typical content-based image retrieval solutions usually cannot achieve satisfactory performance due to the semantic gap challenge. With the popularity of social media applications, large amounts of social images associated with user tagging information are available, which can be leveraged to boost image retrieval. In this paper, we propose a sparse semantic metric learning (SSML) algorithm by discovering knowledge from these social media resources, and apply the learned metric to search relevant images for users. Different from the traditional metric learning approaches that use similar or dissimilar constraints over a homogeneous visual space, the proposed method exploits heterogeneous information from two views of images and formulates the learning problem with the following principles. The semantic structure in the text space is expected to be preserved for the transformed space. To prevent overfitting the noisy, incomplete, or subjective tagging information of images, we expect that the mapping space by the learned metric does not deviate from the original visual space. In addition, the metric is straightforward constrained to be row-wise sparse with the l(2,1)-norm to suppress certain noisy or redundant visual feature dimensions. We present an iterative algorithm with proved convergence to solve the optimization problem. With the learned metric for image retrieval, we conduct extensive experiments on a real-world dataset and validate the effectiveness of our approach compared with other related work.
关键词	Sparse Metric Semantic Distance Metric Social Image Image Retrieval
WOS标题词	Science & Technology ; Technology
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION ; CLASSIFICATION ; CONSTRAINTS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号	WOS:000344065400002
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3360
专题	紫东太初大模型研究中心_图像与视频分析
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Liu, Jing,Li, Zechao,Lu, Hanqing. Sparse semantic metric learning for image retrieval[J]. MULTIMEDIA SYSTEMS,2014,20(6):635-643.
APA	Liu, Jing,Li, Zechao,&Lu, Hanqing.(2014).Sparse semantic metric learning for image retrieval.MULTIMEDIA SYSTEMS,20(6),635-643.
MLA	Liu, Jing,et al."Sparse semantic metric learning for image retrieval".MULTIMEDIA SYSTEMS 20.6(2014):635-643.

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