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	Integrated patch model: A generative model for image categorization based on feature selection
	Xu, Feng ; Zhang, Yu-Jin
	2010-05-06 ; 2010-05-06
关键词	salient patch visual keyword feature selection image categorization generative model EM algorithm CLASSIFICATION Computer Science, Artificial Intelligence
中文摘要	Image categorization could be treated as an effective solution to enable keyword-based image retrieval. In this paper, we propose a novel image categorization approach by learning semantic concepts of image categories. In order to choose representative features and meanwhile reduce noisy features, a three-step feature selection strategy is proposed. First, salient patches are detected. Then all the detected salient patches are clustered and the visual keyword vocabulary is constructed. Finally, the region of dominance and the salient entropy measure are calculated to reduce the similar and non-common noises of salient patches. Based on the selected visual keywords, the Integrated Patch (IP) model is proposed to describe and categorize images. As a generative model, the IP model represents the appearance of the combination of the visual keywords, considering the diversity of the object or the scene. The parameters are estimated by the EM algorithm. The experimental results on the Corel image dataset demonstrate that the proposed feature selection and the image description model are effective in image categorization. (c) 2007 Elsevier B.V. All rights reserved.
语种	英语 ; 英语
出版者	ELSEVIER SCIENCE BV ; AMSTERDAM ; PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/11602]
专题	清华大学
推荐引用方式 GB/T 7714	Xu, Feng,Zhang, Yu-Jin. Integrated patch model: A generative model for image categorization based on feature selection[J],2010, 2010.
APA	Xu, Feng,&Zhang, Yu-Jin.(2010).Integrated patch model: A generative model for image categorization based on feature selection..
MLA	Xu, Feng,et al."Integrated patch model: A generative model for image categorization based on feature selection".(2010).

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