A novel image segmentation algorithm based on visual saliency detection and integrated feature extraction
Wei-Ting Liu, Xue Qing, Jian Zhou · 2016
Since 20th century generation, image segmentation has been attached great importance by the people and thousands of the segmentation algorithms have been proposed. In practice, in order to facilitate research and analysis of the images that often only needs to be interested in certain parts of the image part is divided into a certain properties of a particular area, in order to use the target further, we also need to complete for their separation. This paper integrates the visual attention model to propose the novel image segmentation algorithm based on visual saliency detection and the integrated feature extraction. We have achieved the better numerical performance compared with other state-of-the-art methodologies. The propose method integrates the local and the regional features to construct the global optimized saliency map to serve as the basis for segmentation. The entropy of the image is used for determining the threshold and the effectiveness is proved through experiment.