Abstract Multiinstance learning has already been employed in ContentBased Image Retrieval (CBIR) for the reason that it is good at dealing with the inherent ambiguity of images. In this paper, a ultiinstance learning based CBIR approach is presented. The whole image is regarded as a multiinstance bag. The image is partitioned into several regions using a SelfOrganizing 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 multiinstance 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.
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2006, Vol. 19 
