M.: A weighted distance approach to relevance feedback

Content-based image retrieval systems use low-level fea-tures like color and texture for image representation. Given these representations as feature vectors, similarity between images is measured by computing distances in the feature space. Unfortunately, these low-level features cannot al-ways capture the high-level concept of similarity in human perception. Relevance feedback tries to improve the perfor-mance by allowing iterative retrievals where the feedback information from the user is incorporated into the database search. We present a weighted distance approach where the weights are the ratios of standard deviations of the fea-ture values both for the whole database and also among the images selected as relevant by the user. The feedback is used for both independent and incremental updating of the weights and these weights are used to iteratively refine the effects of different features in the database search. Re-trieval performance is evaluated using average precision and progress that are computed on a database of approx-imately 10,000 images and an average performance im-provement of 19 % is obtained after the first iteration. 1