Color images' segmentation using scale space filter and markov random field

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.

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Linguistic color image segmentation using a hierarchical Bayesian approach

Alarcón

Marroquín

2009

Color Research & Application

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In this work, we combine Bayesian techniques with a color categorization model, which leads to a method for the linguistic segmentation of color images. The categorization model considers the 11 universal color categories proposed by Berlin and Kay [Basic Color Terms: Their Universality and Evolution. Berkeley: University of California;1969]. The likelihood for each category is represented by a linear combination of quadratic splines, and as a result, each voxel in the color space L*u*v* is described as a vector of probabilities, whose components express the degree to which the voxel belongs to a given color category. This gives rise to a probabilistic dictionary which is used for the segmentation, in which prior spatial granularity constraints are incorporated via an entropy-controlled quadratic Markov measure field (ECQMMF) model, as proposed by Rivera et al. [IEEE Trans Image Process 2007;16:3047-3057]. We give a generalization of ECQMMF that allows one to consider the perceptual interactions between the basic colors that were experimentally established by Boynton and Olson [Color Res Appl 1987;12:94-105].

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Color images' segmentation using scale space filter and markov random field

Cited by 62 publications

References 8 publications

Image retrieval by color semantics with incomplete knowledge

Image retrieval by color semantics with incomplete knowledge

Image retrieval by color semantics with incomplete knowledge

Linguistic color image segmentation using a hierarchical Bayesian approach

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