Unsupervised Color Textured Image Segmentation Using Cluster Ensembles and MRF Model

We propose a novel approach to implement robust unsupervised color image content understanding approach that segments a color image into its constituent parts automatically. The aim of this work is to produce precise segmentation of color images using color and texture information along with neighborhood relationships among image pixels which will provide more accuracy in segmentation. Here, unsupervised means automatic discovery of classes or clusters in images rather than generating the class or cluster descriptions from training image sets. As a whole, in this particular work, the problem we want to investigate is to implement a robust unsupervised SVFM model based color medical image segmentation tool using Cluster Ensembles and MRF model along with wavelet transforms for increasing the content sensitivity of the segmentation model. In addition, Cluster Ensemble has been utilized for introducing a robust technique for finding the number of components in an image automatically. The experimental results reveal that the proposed tool is able to find the accurate number of objects or components in a color image and eventually capable of producing more accurate and faithful segmentation and can. A statistical model based approach has been developed to estimate the Maximum a posteriori (MAP) to identify the different objects/components in a color image. The approach utilizes a Markov Random Field model to capture the relationships among the neighboring pixels and integrate that information into the Expectation Maximization (EM) model fitting MAP algorithm. The algorithm simultaneously calculates the model parameters and segments the pixels iteratively in an interleaved manner. Finally, it converges to a solution where the model parameters and pixel labels are stabilized within a specified criterion. Finally, we have compared our results with another well-known segmentation approach.