Visual attention based small object segmentation in natual images
Wen Qiang Guo, Changshen Xu, Songde Ma, Min Gang Xu · 2010
Small object segmentation is a challenging task in image processing and computer vision. In this paper we propose a visual attention based segmentation approach to segment interesting objects with small size in natural images. Different from traditional methods which use the single feature vectors, visual attention analysis is used on local and global features to extract the region of interesting objects. Within the region selected by visual attention analysis, Gaussian Mixture Model (GMM) is applied to further locate the object region. By incorporation of visual attention analysis into object segmentation, the proposed approach is able to narrow the searching region for object segmentation so as to increase the segmentation accuracy and reduce the computational complex. Experimental results demonstrate that the proposed approach is efficient for object segmentation in natural images, especially for small objects. The proposed method outperforms traditional GMM based segmentation significantly.