| This thesis focuses on an intuitive approach to natural scene segmentation. This research uses color and texture features in cooperation to provide comprehensive knowledge about every pixel in the image. A novel scheme for the collection of training samples, based on the notion of homogeneity, is proposed. Natural scene segmentation is carried out using a two-stage hierarchical self-organizing map. The first stage of the network employs a fixed-sized two-dimensional map that captures the dominant color and texture features of an image, in an unsupervised mode. The second stage combines a fixed-sized one-dimensional feature map and color merging, to control the number of color clusters formed as a result of the segmentation. The proposed method confirms that the self-learning ability and adaptability of the self-organizing map, coupled with the information fusion mechanism of the hierarchical network, leads to good segmentation results. These are further confirmed by extensive tests on a variety of natural scene images. |
