Image retrieval by color semantics with incomplete knowledge

Abstract

Retrieval by content from image databases faces the distance between low-level syntactic features that can be automatically detected by conventional image processing tools and high level semantics which captures user's filtering intentions. A system is presented which bridges this gap by resorting to a theory formulated by Johannes Itten in 1960, and widely accepted in the community of fine arts, to support objective interpretation of color arrangements over paintings. The system relies upon a schema distinguishing archiving, querying, and retrieval stages. In the archiving stage, images are associated with a description capturing the spatial arrangement of regions with homogeneous chromatic attributes, as detected by the use of an automatic image processing tool. Imprecise descriptions are supported through the adoption of a hierarchical index providing a multi-resolution representation of image contents. In the querying stage, a visual iconic language allows the expression of sentences about chromatic contents in accordance with a high-level semantic model of colors combinations. By permitting flexible expression of abstract, non-literal, properties, the model supports intentional vagueness and incompleteness in the specification of searching queries. In the retrieval stage, a similarity score is introduced, which accounts for the degree with which a query assertion applies to a given image. The measure of similarity drives the traversal of the hierarchical index up to find the minimum level of description precision, permitting a definite decision about the satisfaction of the query on each stored image. © 1998 John Wiley & Sons, Inc.