A new stochastic model based approach for object identification and segmentation in textured color image

We investigate and propose a novel stochastic model based approach to implement a robust unsupervised color image content understanding technique that segments a color textured image into its constituent parts automatically and meaningfully. The aim of this work is to detection and identification of different objects in a color image using image segmentation. Image segments or objects are produced using precise color information, texture information and neighborhood relationships among neighboring image pixels. As a whole, in this particular work, the problem we want to investigate is to implement a robust Maximum a posteriori (MAP) based unsupervised color textured image segmentation approach using Cluster Ensembles, MRF model and Daubechies wavelet transform for identification and segmentation of image contents or objects. 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 model is able to find the accurate number of objects or components in a color image and can produce more accurate and faithful segmentation of different meaningful objects from relatively complex background. Finally, we have compared our results with another similar existing segmentation approach.