Object formation by learning in visual databases using hierarchical content description

This paper proposes a self-learning content-based image indexing and retrieval system that employs a hierarchical content representation (consisting of objects and regions) 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 off-line analysis of database images into meaningful objects. Once the learning phase is complete, the speed of shape based retrieval of the learned objects in the database increases significantly. Experimental results are presented to show the effectiveness of the proposed hierarchical content representation, hierarchical matching, and the learning behavior on collections of car images