An adaptive neuro-fuzzy system for color image segmentation

Kanchan Deshmukh, G N Shinde


Image segmentation and object extraction plays an important role in image analysis and computer vision. In this paper, we propose a novel technique for color image segmentation called 'adaptive neuro-fuzzy color image segmentation (ANFCIS)'.The proposed system consists of multilayer perceptron (MLP)-like network which performs color image segmentation using multilevel thresholding. Threshold values for detecting clusters and their labels are found automatically using fuzzy C-means (FCM) clustering technique. Fuzzy entropy is used as a tool to decide the number of clusters. ANFCIS uses saturation and intensity planes of HSV (hue, saturation, intensity) color space for segmentation. Neural network is employed to find the number of objects automatically from an image. The major advantage of this method is that it does not require a priori knowledge to segment a color image. The algorithm is found to be robust and relatively computationally inexpensive for large variety of color images. Experimental results have demonstrated the effectiveness and efficiency of the proposed method.


Adaptive thresholding; fuzzy entropy; color image segmentation; neuro-fuzzy system; and clustering

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