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Similarity-based retrieval of images is an important task in many image database applications. A major class of users' requests requires retrieving those images in the database that are spatially similar to the query image. We propose an algorithm for computing the spatial similarity between two symbolic images. A symbolic image is a logical representation of the original image where the image objects are uniquely labeled with symbolic names. Spatial relationships in a symbolic image are represented as edges in a weighted graph referred to as spatial-orientation graph. Spatial similarity is then quantified in terms of the number of, as well as the extent to which, the edges of the spatial-orientation graph of the database image conform to the corresponding edges of the spatial-orientation graph of the query image. The proposed algorithm is robust in the sense that it can deal with translation, scale, and rotational variances in images. The algorithm has quadratic time complexity in terms of the total number of objects in both the database and query images. We also introduce the idea of quantifying a system's retrieval quality by having an expert specify the expected rank ordering with respect to each query for a set of test queries. This enables us to assess the quality of algorithms comprehensively for retrieval in image databases. The characteristics of the proposed algorithm are compared with those of the previously available algorithms using a testbed of images. The comparison demonstrated that our algorithm is not only more efficient but also provides a rank ordering of images that consistently matches with the expert's expected rank ordering.
Gudivada et al. (Sat,) studied this question.