Abstract
Content-based image retrieval has become an increasingly popular field of research in computer vision, however the problem of the semantic gap between low-level features, such as color, shape and texture, and high-level semantics remains unsolved. Recent research in content-based image retrieval has shifted to an interactive process that considers the user as a part of the retrieval process. In order to realize this iterative process, relevance feedback is introduced into content-based image retrieval. The purpose of the present study is, therefore, to develop a query-by-sketch image retrieval method for reducing the semantic gap between low-level features and high-level semantics by adopting relevance feedback. In the proposed method, users' sketches play an important role in reducing the semantic gap by relevance feedback. This method is applied to 6,500 images in Corel Photo Gallery. Experimental results show that the image retrieval system with the relevance feedback is superior to the image retrieval system without the relevance feedback.
