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We present the idea of peer indexing - indexing an image by semantically correlated images - and its application in image retrieval. A learning strategy is suggested for automatic acquisition of peer indices from user feedback, and the similarity metric for the peer index is formulated. A cooperative framework is proposed under which the peer index is integrated with low-level features for image retrieval and relevance feedback. Encouraging results on both short-term and long-term retrieval performance of our approach are shown by experiments.