Feature-based approach for image retrieval by sketch
Yin Chans, Zhibin Lei, Daniel Lopresti, Sun‐Yuan Kung · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
In this paper, we introduce a feature-based approach for image retrieval by sketch. Using edge segments as features which are modeled by implicit polynomials (IPs), we hope to provide a similarity computation method that is robust towards user query sketch distortions. We report some preliminary results of the first phase of our work in this paper. From these results, we could see that the feature-based method, which currently uses edge features modeled by first degree IPs, generally performs better than a pixel-based method that has been adopted by a number of well-known content-based image indexing and retrieval systems such as the IBM QBIC project. The feature-based method appears more robust and tolerant towards distortions in query sketches. We attribute this quality to the fact that it uses more structural information to compute the similarities between images. We will also describe a prototype built upon the Java technology that allows queries over the WWW. Finally we will discuss the promise and limitations of the feature-based method and conclude the paper with a look at areas for future work.