An Efficient Search Algorithm for Content-Based Image Retrieval with User Feedback

We propose a probabilistic model for the relevance feedback of users looking for target images. This model takes into account user errors and user uncertainty about distinguishing similarly relevant images. Based on this model, we have developed an algorithm, which selects images to be presented to the user for further relevance feedback until a satisfactory image is found. In each query session, the algorithm maintains weights on the images in the database which reflect the assumed relevance of the images. Relevance feedback is used to modify these weights. As a second ingredient, the algorithm uses a minimax principle to select images for presentation to the user: any response of the user will provide significant information about his query, such that relatively few feedback rounds are sufficient to find a satisfactory image. We have implemented this algorithm and have conducted experiments on both simulated data and real data which show promising results.