Self-Organizing Map-Based Color Image Segmentation with k-Means Clustering and Saliency Map
Figure 2
Clustered SOM nodes (a) and hit map (b) of image 295087. Color features , , and of the image 295087 (see Figure 6) are processed with the proposed SOM-K method. Pixels are clustered (or quantized) into 81 prototype vectors (nodes), whose colors (RGB in prototype vectors) are shown in (a). As can be seen in image 295087, there are mainly two colors with different tones, brown and blue in (a). Each prototype vector is a representative of a group of pixels. Total 81 representatives are further clustered with k-means into 2 clusters marked with × and ○. The right figure (b) shows the hits map of the SOM. The larger the black area in each node, the more it is hit by input patterns. The hits map is used as a filter to delete the prototype vectors. Those prototype vectors with zero hits are deleted, because they do not represent any input patterns.
