The purpose of this paper is to show how the edge histogram descriptor for MPEG-7 can be efficiently utilized for image matching. Since the edge histogram descriptor recommended for the MPEG-7 standard represents only local edge distribution in an image, the matching performance for image retrieval may not be satisfactory. In this paper, to increase the matching performance, we propose to use the global and semi-local edge histograms generated directly from the local histogram bins. Then, the global, semi-global, and local histograms of two images are compared to evaluate the similarity measure. Since we exploit the absolute locations of edge in the image as well as its global composition, the proposed matching method is considered to be a more image content-based retrieval. Experimental results support this claim. Experiments on test images for MPEG-7 core experiment show that the proposed method yields better retrieval performance especially for semantic similarity.
In this paper, a relevance feedback method is proposed to improve the retrieval performance of the MPEG-7 edge histogram descriptor. Specifically, by updating the query point and the weighting factors simultaneously, we can reduce the convergence speed of the relevance feedback retrieval. Experimental results show that the proposed method improves the performance significantly for natural and clip art images.
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