A noise-free similarity model for image retrieval systems

Reducing noise (i.e., irrelevant regions) in image query processing is no doubt one of the key elements to achieve high retrieval effectiveness. However, existing techniques are not able to eliminate noise from similarity matching since they capture the features of the entire image area or pre-perceived objects at the database build time. In this paper, we address this outstanding issue by proposing a similarity model for noise-free queries. In our approach, users formulate their queries by specifying objects of interest, and image similarity is based only on these relevant objects. We discuss how our approach can handle translation and scaling matching as well as how space overhead can be minimized. Our experiments show that this approach, with 1/16 the storage overhead, outperforms techniques for rectangular queries and a related technique by a significant margin.