一种基于多示例学习的图像检索方法<sup>*</sup>

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Abstract Multiinstance learning has already been employed in ContentBased Image Retrieval (CBIR) for the reason that it is good at dealing with the inherent ambiguity of images. In this paper, a ultiinstance learning based CBIR approach is presented. The whole image is regarded as a multiinstance bag. The image is partitioned into several regions using a SelfOrganizing feature Map (SOM) clustering based image segmentation method, then the regions described by color and texture features are regarded as the instances in the bag. Next, query images posed by the user are transformed into corresponding positive and negative bags and a multiinstance algorithm is employed for image retrieval and relevance feedback. Experiments show that this approach achieves comparable results to some existing approaches and is even more efficient.

Key words： Machine Learning MultiInstance Learning Content Based Image Retrieval

Received: 16 November 2004

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	DAI HongBin
	ZHANG MinLing
	ZHOU ZhiHua

Cite this article:

DAI HongBin,ZHANG MinLing,ZHOU ZhiHua. A MultiInstance Learning Based Approach to Image Retrieval[J]. , 2006, 19(2): 179-185.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2006/V19/I2/179

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