Data and Model-Driven Selection using Color Regions

A key problem in model-based object recognition is selection, namely, the problem of determining which regions in an 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). It presents a method of color specification by color categories which are used to design a fast segmentation algorithm to extract perceptual color regions. Data driven selection is then achieved by selecting salient color regions while model-driven selection is achieved by locating instances of the model in the image using the color region description of the model. The approach presented here 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.