Indexing and retrieval of images by spatial constraints

Abstract

Many multimedia applications require retrieval of spatially similar images against a given query image. Existing work on image retrieval and indexing either requires extensive low-level computations or elaborate human interaction. In this paper, we introduce a new symbolic image representation technique to eliminate repetitive tasks of image understanding and object processing. Our symbolic image representation scheme is based on the concept of hierarchical decomposition of image space into spatial arrangements of features while preserving the spatial relationships among the image objects. Quadtrees are used to manage the decomposition hierarchy and play an important role in defining the similarity measure. This scheme is incremental in nature, can be adopted to accommodate varying levels of details in a wide range of application domains, and provides geometric variance independence. While ensuring that there are no false negatives, our approach also discriminates against non-matching entities by eliminating them as soon as possible, during the coarser matching phases. A hierarchical indexing scheme based on the concept of image signatures and efficient quadtree matching has been devised. Each level of the hierarchy tends to reduce the search space, allowing more involved comparisons only for potentially matching candidate database images. For a given query image, a facility is provided to rank-order the retrieved spatially similar images from the image database for subsequent browsing and selection by the user.