Hidden-Concept Driven Multilabel Image Annotation and Label Ranking

	Hidden-Concept Driven Multilabel Image Annotation and Label Ranking
	Bao, Bing-Kun1 ; Li, Teng 1; Yan, Shuicheng 2
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
	2012-02-01
卷号	14 期号:1 页码:199-210
文章类型	Article
摘要	Conventional semisupervised image annotation algorithms usually propagate labels predominantly via holistic similarities over image representations and do not fully consider the label locality, inter-label similarity, and intra-label diversity among multilabel images. Taking these problems into consideration, we present the hidden-concept driven image annotation and label ranking algorithm (HDIALR), which conducts label propagation based on the similarity over a visually semantically consistent hidden-concepts space. The proposed method has the following characteristics: 1) each holistic image representation is implicitly decomposed into label representations to reveal label locality: the decomposition is guided by the so-called hidden concepts, characterizing image regions and reconstructing both visual and nonvisual labels of the entire image; 2) each label is represented by a linear combination of hidden concepts, while the similar linear coefficients reveal the inter-label similarity; 3) each hidden concept is expressed as a respective subspace, and different expressions of the same label over the subspace then induce the intra-label diversity; and 4) the sparse coding-based graph is proposed to enforce the collective consistency between image labels and image representations, such that it naturally avoids the dilemma of possible inconsistency between the pairwise label similarity and image representation similarity in multilabel scenario. These properties are finally embedded in a regularized nonnegative data factorization formulation, which decomposes images representations into label representations over both labeled and unlabeled data for label propagation and ranking. The objective function is iteratively optimized by a convergence provable updating procedure. Extensive experiments on three benchmark image datasets well validate the effectiveness of our proposed solution to semisupervised multilabel image annotation and label ranking problem.
关键词	Image Annotation Label Ranking Nonnegative Data Factorization
WOS标题词	Science & Technology ; Technology
关键词[WOS]	NONNEGATIVE MATRIX FACTORIZATION ; CLASSIFICATION ; SELECTION ; FEATURES
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000302701100019
引用统计	被引频次：18[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/173211/8009
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100049, Peoples R China 2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117548, Singapore
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Bao, Bing-Kun,Li, Teng,Yan, Shuicheng. Hidden-Concept Driven Multilabel Image Annotation and Label Ranking[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2012,14(1):199-210.
APA	Bao, Bing-Kun,Li, Teng,&Yan, Shuicheng.(2012).Hidden-Concept Driven Multilabel Image Annotation and Label Ranking.IEEE TRANSACTIONS ON MULTIMEDIA,14(1),199-210.
MLA	Bao, Bing-Kun,et al."Hidden-Concept Driven Multilabel Image Annotation and Label Ranking".IEEE TRANSACTIONS ON MULTIMEDIA 14.1(2012):199-210.