Visual Attention Guided Seed Selection for Color Image Segmentation

The ”seeded region growing” (SRG) is a segmentation technique which performs an image segmentation with respect to a set of initial points, known as seeds. Given a set of seeds, SRG then grows the regions around each seed, based on the conventional region growing postulate of similarity of pixels within regions. The choice of the seeds is considered as one of the key steps on which the performance of the SRG technique depends. Thus, numerous knowledge-based and pure data-driven techniques have been already proposed to select these seeds. This paper studies the usefulness of visual attention in the seed selection process for performing color image segmentation. The purely data-driven visual attention model, considered in this paper, provides the required points of attention which are then used as seeds in a SRG segmentation algorithm using a color homogeneity criterion. A first part of this paper is devoted to the presentation of the multicue saliency-based visual attention model, which detects the most salient parts of a given scene. A second part discusses the possibility of using the so far detected regions as seeds to achieve the region growing task. The last part is dedicated to experiments involving a variety of color images.