Data and Model-Driven Selection Using Color Regions

Abstract

A key problem in model-based object recognition is selection, namely, the problem of determining which regions in the image are likely to come from a single object. In this paper we present an approach that uses color as a cue to perform selection either based solely on image-data (data-driven), or based on the knowledge of the color description of the model (model-driven). Specifically, the paper presents a method of color specification in terms of perceptual color categories and shows its relevance for the task of selection. The color categories are used to develop a fast region segmentation algorithm that extracts perceptual color regions in images. The color regions extracted form the basis for performing data and model-driven selection. Data-driven selection is achieved by selecting salient color regions as judged by a color-saliency measure that emphasizes attributes that are also important in human color perception. The approach to model-driven selection, on the other hand, exploits the color and other region information in the 3d model object to locate instances of the object in a given image. The approach presented tolerates some of the problems of occlusion, pose and illumination changes that make a model instance in an image appear different from its original description. Finally, the utility of color-based selection is demonstrated by showing the extent of search reduction possible when color-based selection is integrated with a recognition system.