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	Boosted Inductive Matrix Completion for Image Tagging
	Hou, Yuqing
	2016
关键词	Image tag completion Boosted inductive matrix completion Visual-tag correlation Semantic-tag correlation CNN features Word vectors TAG COMPLETION ANNOTATION
英文摘要	Search engines have traditionally used manual image tagging for indexing and retrieving image collections. Manual tagging is expensive and labor intensive, motivating the research on automatic tag completion. However, existing tag completion approaches suffer from deficient or inaccurate tags. In this study, we formulate the task in the boosted inductive matrix completion (BIMC) framework, which combines the power of the inductive matrix completion (IMC) model together with a standard matrix completion (MC) model. We incorporates visual-tag correlation and semantic-tag correlation properties into the model for better exploration of the latent connection between image features and tags. We exploit CNN features and word vectors to narrow the semantic gap. The proposed method achieves good performance on several benchmark datasets with missing and noisy tags.; EI; CPCI-S(ISTP); houyuqing1988@gmail.com; 92-99; 9719
语种	英语
出处	13th International Symposium on Neural Networks (ISNN)
DOI标识	10.1007/978-3-319-40663-3_11
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449585]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Hou, Yuqing. Boosted Inductive Matrix Completion for Image Tagging. 2016-01-01.

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