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	Image Annotation Incorporating Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors
	Hou, Yuqing
	2015
关键词	RETRIEVAL COMPLETION RECOGNITION ALGORITHM OBJECT
英文摘要	Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags. However, TBIR is still suffering from the incomplete and inaccurate tags provided by users, posing a great challenge for tag-based image management applications. In this work, we propose a novel method for image annotation, incorporating several priors: Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors. Highly representative CNN feature vectors are adopted to model the tag-visual correlation and narrow the semantic gap. And we extract word vectors for tags to measure similarity between tags in the semantic level, which is more accurate than traditional frequency-based or graph-based methods. We utilize the Accelerated Proximal Gradient (APG) method to solve our model efficiently. Extensive experiments conducted on multiple benchmark datasets demonstrate the effectiveness and robustness of the proposed method.; EI; CPCI-S(ISTP); houyuqing1988@gmail.com; 71-81; 9474
语种	英语
出处	ADVANCES IN VISUAL COMPUTING, PT I (ISVC 2015)
DOI标识	10.1007/978-3-319-27857-5_7
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436886]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Hou, Yuqing. Image Annotation Incorporating Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors. 2015-01-01.

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