INTERACTIVE REGION-BAESD IMAGE RETRIEVAL

Abstract

This paper proposes an interactive region-based image retrieval system. Initially, we use color clustering by K-means algorithm and region labeling to segment an image into regions. Several geometric invariant features, such as dominant color, color histogram, moment invariants, and co-occurrence texture features, are extracted to index each region. Then, we describe each image as a combination of feature vectors of the segmented regions. To measure the distance between images, we define a hierarchical distance function as a liner combination of region features. The retrieved results can be refined via interactive relevance feedback. To learn the “ideal” query regions that the users really want, we derive the weighting parameters of distance measurement using optimized learning technique. A series of experiments have been performed to demonstrate the effectiveness and performance of our work.