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.

Qualitative distances and qualitative description of images for indoor scene description and recognition in robotics

“…Stanchev et al [2003] defined 12 fundamental colours based on the Luv colour space and used Johannes Itten's colour theory to define light-dark contrast, warm-cool contrast, etc. Corridoni et al [1998] presented a model for colour naming based on the HSL colour space and also introduced some semantic connotations as warm/cool or light/dark colours. Lammens [1994] presented a computational model for colour perception and colour naming based on CIE XYZ, CIE Lab and NPP colour spaces.…”

Digital Images in United States and Canadian Dental Education: The Role of Delivery Medium and Metadata

“…The majority of image retrieval research currently is focused on developing more robust content-based systems of image retrieval employing advanced techniques such as querying by visual contents using spatial arrangement and chromatic attribute similarity (Corridoni, Bimbo & Vicario, 1998), and shape feature extraction methods (Li, Lee & Adjeroh, 2005) to name a select few. Comprehensive discussion of content-based retrieval is beyond the focus of this paper, and the research much too numerous to report in this space.…”