Image Segmentation Method Based On Finite Doubly Truncated Bivariate Gaussian Mixture Model with Hierarchical Clustering

Image segmentation is one of the most important area of image retrieval. In colour image segmentation the feature vector of each image region is ’n’ dimension different from grey level image. In this paper a new image segmentation algorithm is developed and analyzed using the finite mixture of doubly truncated bivariate Gaussian distribution by integrating with the hierarchical clustering. The number of image regions in the whole image is determined using the hierarchical clustering algorithm. Assuming that a bivariate feature vector (consisting of Hue angle and Saturation) of each pixel in the image region follows a doubly truncated bivariate Gaussian distribution, the segmentation algorithm is developed. The model parameters are estimated using EM-Algorithm, the updated equations of EM-Algorithm for a finite mixture of doubly truncated Gaussian distribution are derived. A segmentation algorithm for colour images is proposed by using component maximum likelihood. The performance of the developed algorithm is evaluated by carrying out experimentation with six images taken form Berkeley image dataset and computing the image segmentation metrics like, Probabilistic Rand Index (PRI), Global Consistency Error (GCE), and Variation of Information (VOI). The experimentation results show that this algorithm outperforms the existing image segmentation algorithms.