An Algorithm Based on Self-Organizing Kohonen’s SOM for Color Image Segmentation

Abstract:

The paper proposes an algorithm based on the self-organizing Kohonen’s SOM to resolve the difficulties brought by the information fusion in the color image segmentation. First, considering the relationship of NBS distance and human perception, the image’s information is transformed from the RGB to the Munsell color space. Combining the spatial information, the initial segmented regions are formed by the kohonen’s SOM training according to the computational method of distance provided in the paper. Second, the initial regions are merged until the termination rule of the merging process is contented. The algorithm syncretizes the color and spatial information, which is demonstrated that segmentation results hold favorable consistency in term of human perception consistency by a great deal of experiments.

• Periodical: Advanced Materials Research (Volumes 926-930)
• Pages: 2942-2946
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.926-930.2942
• Online since: May 2014
• Authors: Ting Ting Wang*, Wei Dong
• Keywords: Kohonen’s SOM, Model Distance, Munsell Color Space, NBS Distance, Region Merging

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DOI: 10.1109/cvpr.1998.698664