In this paper we explore methods for spatial and feature normalization of visual descriptors for content-based retrieval (CBR). A great many descriptors have been developed for characterizing features such as color, texture, edges, and so forth. In addition, numerous methods have also been proposed for extracting descriptors from whole images or regions. Furthermore, different options are possible for normalizing descriptor values for matching. In this paper, we study different spatial and feature normalization strategies that include extracting descriptors from different spatial partitionings and normalizing descriptor values based on metric-space considerations or statistics of image collections. We empirically evaluate the relative efficacy in an image retrieval testbed.
