The present paper describes a query-by-sketch image retrieval system aimed at reducing the semantic gap by adopting relevance feedback. To reduce the semantic gap between low-level visual features and high-level semantics, in this content-based image retrieval system, users' sketches play an important role in relevance feedback. When users mark similar images of output images with "relevant" labels, the "relevant" images are relevant to the sketch image in positive feedback. This method was applied to 5,500 images in Corel Photo Gallery. Experimental results show that the proposed method is effective in retrieving images.

Date Published: 6 December 2005
PDF: 9 pages
Proc. SPIE 6051, Optomechatronic Machine Vision, 60510Z (6 December 2005); doi: 10.1117/12.647689

Published in SPIE Proceedings Vol. 6051:
Optomechatronic Machine Vision
Kazuhiko Sumi, Editor(s)