Hierarchical color clustering for segmentation of textured images

The paper describes a hierarchical color clustering approach for the segmentation of textured images. It is a bottom-up type split and merge operation performed in two steps: first, the operation is carried out using the K-means algorithm in the local windows of an input image to capture the local textural primitives. This is performed in the (R,G,B)-color space. This results in two color classes per local window. Second, the merge operation is employed using the same K-means algorithm module to group the pattern classes resulting from the split operation. This forms the global boundaries of the texture fields present in the input scene, The proposed algorithm is also suitable for a special purpose VLSI chip implementation