FUZZY WAVELET-BASED COLOR IMAGE SEGMENTATION USING SELF-ORGANIZING NEURAL NETWORK

Image segmentation has been and is likely to be an important component of the content-based image acquisition and retrieval systems This paper describes a new method for segmentation of color images The proposed method uses two phases segmentation processes In the 1(st) phase, segmentation is performed with the help of cluster validity measures and Spatial Fuzzy C Mean (sFCM) HSV model helps in the decomposition of color image then FCM is applied separately on each component of HSV model In the 2(nd) phase, for fine tuning, Kohonen's Self Organizing Map (SOM) neural network along with wavelets is used SOM is a computationally expensive network It has been observed that if SOM training performed on the wavelet-transformed image, then not only it reduces SOM training time but in this way makes more compact segments The advantages of new method are (i) it yields regions more homogeneous than those of other methods for color images, (ii) it reduces the spurious blobs, and (iii) it removes noisy spots The technique presented in this paper as a powerful method for noisy color image segmentation and works for both single and multiple-feature data Experiments were performed on standard color images Experiments show better performance of the proposed method when compared with other approaches in practice