Abstract
A novel unsupervised strategy for content-based image retrieval is presented. It is based on a meaningful segmentation procedure that can provide proper distributions for matching via the earth mover's distance as a similarity metric. The segmentation procedure is based on a hierarchical watershed-driven algorithm that extracts meaningful regions automatically. In this framework, the proposed robust feature extraction and the many-to-many region matching along with the novel region weighting for enhancing feature discrimination play a major role. Experimental results demonstrate the performance of the proposed strategy.
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