Color image segmentation using local histogram and self-organization of Kohonen feature map
Segmentation is an important step for image analysis, but a good segment algorithm which can handle color image with texture area and has less computation time is rare. We propose to use the local window image histogram, which is easy to compute and could quickly collect the information of neighbors, together with the Self-Organization of Kohonen Feature Map (SOFM) neural network, which can efficiently cluster data and has parallel hardware structure, as a segmentation kernel. Under the Euclidean distance function with input data normalization and the simplified Mahalanobis distance function, this algorithm will have very good segmentation results for natural images either full with texture or mixed with smooth scenes.