A NSGA Based Approach for Content Based Image Retrieval

The purpose of CBIR (Content Based Image Retrieval) systems is to allow users to retrieve pictures related to a semantic concept of their interest, when no other information but the images themselves is available. Commonly, a series of images are presented to the user, who judges on their relevance. Several different models have been proposed to help the construction of interactive systems based on relevance feedback. Some of these models consider that an optimal query point exists, and focus on adapting the similarity measure and moving the query point so that it appears close to the relevant results and far from those which are non-relevant. This implies a strong causality between the low level features and the semantic content of the images, an assumption which does not hold true in most cases. In this paper, we propose a novel method that considers the search as a multi-objective optimization problem. Each objective consists of minimizing the distance to one of the images the user has considered relevant. Representatives of the Pareto set are considered as points of interest in the search space, and parallel searches are performed for each point of interest. Results are then combined and presented to the user. A comparatively good performance has been obtained when evaluated against other baseline methods.