Perceptual Object Extraction Based on Saliency and Clustering

Abstract

Object-based visual attention has received an increasing interest in recent years. Perceptual object is the basic attention unit of object-based visual attention. The definition and extraction of perceptual objects is one of the key technologies in object-based visual attention computation model. A novel perceptual object definition and extraction method is proposed in this paper. Based on Gestalt theory and visual feature integration theory, perceptual object is defined using homogeneity region, salient region and edges. An improved saliency map generating algorithm is employed first. Based on the saliency map, salient edges are extracted. Then graph-based clustering algorithm is introduced to get homogeneity regions in the image. Finally an integration strategy is adopted to combine salient edges and homogeneity regions to extract perceptual objects. The proposed perceptual object extraction method has been tested on lots of natural images. Experiment results and analysis are presented in this paper also. Experiment results show that the proposed method is reasonable and valid.