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We propose a generic, unsupervised feature classification and image segmentation framework, where only the number of classes is assumed as known. Image segmentation is treated as an optimization problem. The framework involves block-based unsupervised clustering using k-means, followed by region growing in spatial domain. High confidence statistical criteria are used to compute a map of initial labelled pixels. A new region growing algorithm is introduced, which is named Independent Flooding Algorithm and computes a height per label for each one of the unlabeled pixels, using Bayesian dissimilarity criteria. Finally, a MRF model is used to incorporate the local pixel interactions of label heights and a graph cuts algorithm performs the final labelling by minimizing the underlying energy. Segmentation results using texture, intensity and color features are presented.