Interpretation of users' feedback via swarmed particles for content-based image retrieval

While providing relevance feedback (RF) by users proves to be an effective method for content-based image retrieval, how to interpret and learn from the user-provided feedback, however, remains an unsolved problem. In this paper, we propose an integrated users-feedback and learning algorithm by screening individual elements of content features and driving a group of swarmed particles inside the feature space to provide a possible solution. In comparison with the existing approaches, the proposed algorithm achieves a number of advantages, which can be highlighted as: (i) interpretation of users' feedback is independent of both the content features and relevance feedback schemes, and hence the proposed algorithm can be applicable to any content features and relevance feedback methods; (ii) the RF interpretation is followed by a group of swarmed particles, acting as multiple agents rather than a single query image in searching for the desirable images; (iii) the proposed RF interpretation and learning is exploited not only in reweighting the content similarity measurement, but also in regrouping the database images. Extensive experiments support that our proposed algorithm outperforms the existing representative techniques, providing good potential for further research and development for a wide range of content-based image retrieval applications.