Abstract
A new region filtering and region weighting method, which filters out unnecessary regions from images and learns region importance from the region size and the spatial location of regions in an image, is proposed based on region representations. It weights the regions optimally and improves the performance of the region-based retrieval system based on relevance feedback. Due to the semantic gap between the low level feature representation and the high level concept in a query image, semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters in the feature space. Our main goal is finding semantically related clusters and their weights to reduce this semantic gap. Experimental results demonstrate the efficiency and effectiveness of the proposed region filtering and weighting method in comparison with the area percentage method and region frequency weighted by inverse image frequency method, respectively.
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