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Object-based image retrieval through learning from user search patterns and profiles

In this paper, we propose a self-learning content-based image indexing and retrieval system. Our system employs a hierarchical content representation and a hierarchical content matching method for effective and efficient image/object retrieval. the 'learning' behavior is enabled by our proposed hierarchical content representation which allows easy storage of combinations of regions that have resulted in successful matches to objects of interest as determined by user search patterns and profiles. The learning step effectively performs an automatic analysis of database images into meaningful objects given certain user search patterns and interest profiles. The advantages of the proposed hierarchical content representation and 'learning' schemes are demonstrated on a collection of car and face images, where the significant improvements in search and retrieval speed are described both theoretically and experimentally.