Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 16, Issue 4, Pages 925-936Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2005.849822
Keywords
color clustering; color leaning; color reduction; color space; reduced Coulomb energy (RCE); self-organizing map (SONI); simulated annealing (SA); supervised segmentation; unsupervised segmentation
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An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L*u*v* color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks.
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