Segmentation of Natural Scenes Based on Visual Attention and Gestalt Grouping Laws

Detection of salient regions in images of natural scenes can be applied as a pre-processing step for computer vision algorithms as image segmentation, content based image retrieval, object recognition or image compression. This paper presents a visual attention method that analyses the input image in multiple scales using stability information of image regions. The saliency maps constructed have the advantage of preserving well-defined boundaries and making a better separation between background and the salient object, without suffering from undesired effects of multi-scale approaches. Furthermore, a segmentation approach that models Gestalt grouping laws is also applied. The experiments using a database containing 1,000 images showed that our saliency maps outperform the results obtained by other seven visual attention algorithms in terms of F-Measure. In addition, our segmentation approach obtained better results if compared to three classic thresholding algorithms.