Optimal adaptive learning for image retrieval

Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval. However, the existing approaches either require prior knowledge of the data or entail high computation costs, making them less practical. To overcome these difficulties and motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval. Specifically, we cast the image retrieval problem in the optimal filtering framework, which does not require prior knowledge of the data, supports incremental learning, is simple to implement and achieves better performance than state-of-the-art approaches. To evaluate the effectiveness and robustness of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images. We report promising results on a wide variety of queries.