Contentbased sub-image retrieval using relevance feedback

This thesis deals with the problem of £nding images that contain a given query sub-image, the so-called Content-Based sub-Image Retrieval (CBsIR) problem. We propose a scheme named the Hierarchical Tree Matching (HTM), which relies on a hierarchical tree that encodes the color features of image tiles stored in turn as an index sequence. The index sequences of both candidate images and the query sub-image are then compared using a search strategy based on the hierarchical tree structure in order to rank the database images with respect to the query. Our experimental results on a database of over 10,000 images and disk-resident metadata suggest that the HTM scheme can be very effective and ef£cient and performs much better than an alternative method in retrieving the original images, i.e., the ones from which the query sub-images are extracted. To further improve the quality of retrieval, we also investigate the use of feedback to better capture the user’s intention. The user can thus provide feedback on the retrieved results by identifying images of his/her interest. Combined with the HTM strategy, we use a relevance feedback approach based on a tile re-weighting scheme. Our experiments show that this learning approach is quite effective, improving the retrieval within very few iterations