Image Segmentation Algorithm Based on Improved Visual Attention Model and Region Growing

Abstract

The essence of image segmentation is a based on some properties the process for pixel classification. Firstly, three typical methods in image segmentation methods are outlined and their characteristic is analyzed in this paper. The traditional visual attention model is described and improved in this paper. The input image gray value and edge features are extracted by Gabor filters and the Gauss - Laplace operator, gray feature maps and the edge feature maps are got respectively, then interest regions image is obtained by linear combination. The interest region in interest region image is selected by the dynamic neural network methods in artificial intelligence. The limit scope of regional growth is provided improved visual attention model algorithm identified interest region, the binary image is got by setting gray-value. At last, image segmentation is achieved by image segmentation algorithm based improved visual attention model and region growing. Experimental results validate that this methods not only achieve image segmentation, but also accurately and automatically achieve interest region segmentation, improve the quality of the segmentation, and has good robustness.