Abstract
This paper proposes a novel method for content-based image retrieval based on interest points. Interest points are detected from the scale and rotation normalized image. Then the normalized image is divided into a series of sector sub-regions with different area according to the distribution of interest points. With robustness to the image's rotation, scale and translation, local features of every sector sub-region are extracted to describe the image and make the similarity measure. In the relevant feedback phase, images are regarded as multi-instance (MI) bags, and the MI learning algorithm is employed to compute the target image feature. Finally, the similarity is recalculated. Experimental results show that our method can effectively describe the image, and obviously improve the average retrieval precision.
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