In this paper, we propose a method for calculating the similarity between two digital images. A global signature descring the texture, shape, or color content is first computed for every image stored in a database, and a normalized distance between probability density functions of feature vectors is used to match signatures. This method can be used to retrieve images from a database that are similar to an example target image. This algorithm is applied to the problem of search and retrieval for a database containing pulmonary CT imagery, and experimental results are provided.
P.M. Kelly and T.M. Cannon. CANDID: Comparison Algorithm for Navigating Digital Image Databases. In Proceedings of the Seventh International Working Conference on Scientific and Statistical Database Management, pages 252-258. Charlottesville, VA, September, 1994. Los Alamos National Laboratory Technical Report LA-UR-94-0721. [ Abstract | PDF (190 KB) ]
