Scale estimate of self-organizing map for color image segmentation

Self-Organizing Maps (SOM) have presented excellent effect in color image segmentation; the scale of SOM will directly affect the accuracy of segmentation results. In this paper, we proposed a novel scale estimated of self-organizing map (SE-SOM) for color image segmentation based on SOM clustering. Different from conventional SOM model, it determines the number of nodes of competition layer by 3-D spatial distribution of pixels in HSV (Hue-Saturation-value) color space. Then sample pixels to train the map topology of the image and segment pixels by computing similarity between their feature vectors with weights of each node. Finally, design a connectivity filter to update labels of image to decrease noise. Statistical information are used to design map scale, which adapted the final SOM scale to the distribution feature of pixels, clustering results more accurate and stable, Experiments results show that the algorithm can produce ideal results with manual segmentation and suitable PNSR values.