Regions-of-Interest and Spatial Layout for Content-Based Image Retrieval

To date most content-based image retrieval (CBIR) techniques rely on global attributes such as color or texture histograms which tend to ignore the spatial composition of the image. In this paper, we present an alternative image retrieval system based on the principle that it is the user who is most qualified to specify the query content and not the computer. With our system, the user can select multiple regions-of-interest and can specify the relevance of their spatial layout in the retrieval process. We also derive similarity bounds on histogram distances for pruning the database search. This experimental system was found to be superior to global indexing techniques as measured by statistical sampling of multiple users' satisfaction ratings.