A connectionist approach for color image segmentation

A connectionist clustering strategy is presented for segmenting color images. First the local peaks in the 3-D red, green, blue histogram are located. Then using these as the prototypes other patterns are classified into one of them. The prototype selection method employs only neuronal dynamics and therefore is faster than existing clustering neural networks. The classification network takes into account the distribution of the data and hence is less prone to misclassifications. Experimental results obtained by applying the network for segmenting one color image are presented