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Color Image Segmentation Using Hybrid Learning Techniques

Image segmentation is the process of finding out all non-overlapping distinct regions from the given image based on certain criteria such as intensity, color, texture or shape. This paper proposes a two level hybrid non classical model for image segmentation based on pixel color and texture features of the image. The first level uses Fuzzy C-Means (FCM) unsupervised method to form a clustering among all the pixels based on color and texture properties. And the second level uses supervised methods [Classification Tree and Adaboost] for color learning and pixel classification, with the prior knowledge of the image obtained from the FCM. Experiment was conducted for the three different supervised classification methods including Support Vector Machine (SVM) and their performances are analysed. From the results, it is inferred that Adaboost classifier increases accuracy and reduces misclassification error when compared to Classification Tree and SVM methods.