Robust Image Retrieval in a Statistical Framework

In this report, we focus on robust image retrieval. We rst describe why robustness is of importance in this kind of search. Then we propose a very general architecture based on three steps. As an assumption, we assume that any image of the database is characterized by a set of features (each feature resulting in a statistical distribution of a particular measure over the image). The rst step of the process consists in computing, for any characteristic, a similarity/dissimilarity measure between the distribution related to the image request and those of the images in the database. This results in a set of scalar values that have to be summarize in a 1D value in order to easily sort the image with respect to the request. This is what the second step is about. Even when powerful tools (i.e. robust: : :) are used, given the m best matches, it can happen that part of the answers is not satisfactory. Therefore the third step intends to increase the robustness of the overall process using a feedback analysis (which can be supervised or non supervised). This step mainly tries to emphasize on the role of the more discriminant characteristics. We will show in this report some approaches to improve the robustness of these three steps.