Granule-view based feature extraction and classification approach to color image segmentation in a manifold space

Sound segmentation technology is the central in image analysis and computer vision. In this paper, a granule-based approach to color image segmentation is introduced, formulated by combining color jump connection-based granule construction with manifold learning-based feature extraction technique. Aiming at characterizing irregular objects in an image, a granulitized model is established by using techniques of jump connected segmentation and morphological reconstruction to accurately represent objects. Laplacian eigenmaps (LE) manifold learning technique is applied to extract features of granules automatically, which allows taking smooth and texture information into consideration effectively. Markov Chain Monte Carlo (MCMC) method is explored for the process of granule merging. Experiments demonstrate that the proposed approach to color image segmentation reaches high precision of granulitized representation and reliable feature characterization of objects, and yields promising segmentation results.