Combining stroke-based and selection-based relevance feedback for content-based image retrieval

We propose a flexible interaction mechanism for CBIR by enabling relevance feedback inside images through drawling strokes. User's interest is obtained from an easy-to-use user interface, and fused seamlessly with traditional feedback information in a semi-supervised learning framework. Retrieval performance is boosted due to more precise description of the query concept. Region segmentation is also improved based on the collected strokes, and further enhances the retrieval precision. We implement our system Flexible Image Search Tool (FIST) based on the ideas above. Experiments on two real world data sets demonstrate the effectiveness of our approach.