Constrained Compound MRF Model with Bi-Level Line Field for Color Image Segmentation

Image segmentation is a basic early vision problem which serves as precursor to many high level vision problems. Color image segmentation provides more information while solving high level vision problems such as, object recognition, shape analysis etc. Therefore, the problem of color image segmentation has been addressed more vigorously for more than one decade. Different color models such as RGB, HSV, YIQ, Ohta (I1, I2, I3), CIE(XYZ, Luv, Lab) are used to represent different colors [5]. From the reported study, HSV and (I1, I2, I3) have been extensively used for color image segmentation. Ohta color space is a very good approximation of the Karhunen-Loeve transformation of the RGB, and is very suitable for many image processing applications [1]. Image Modeling plays a crucial role in image anal‐ ysis. Stochastic models, particularly MRF models, have been successfully used as the image model for image restoration and segmentation [2], [3], [4]. MRF model has also been success‐ fully used as the image model while addressing the problem of color image segmentation both in supervised and unsupervised framework. Kato et al [6] have proposed a MRF model based unsupervised scheme for color image segmentation. In Kato 's method, the model pa‐ rameters have been estimated using Maximum Likelihood criterion and the only parameter identified by the user is the number of class. This algorithm could be validated using differ‐ ent color textures and real images. Another color texture unsupervised segmentation algo‐ rithm has been proposed by Deng et al [7] and the method has been retermed as JSEG method. Recently, an unsupervised image segmentation algorithm has been proposed by Guo et al [8] where K-means has been used to initialize the classification in the classification of numbers. Very recently Scarpa et al. [13] have proposed a multiscale texture model and a related algorithm for the unsupervised segmentation of color images. In this scheme, the feature vectors have been collected and based on the feature vector the textures are then re‐