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The First IEEE International Workshop on Electronic Design, Test and Applications (DELTA '02)
On the Initialization and Training Methods for Kohonen Self-Organizing Feature Maps in Color Image Quantization
Christchurch, New Zealand
January 29-January 31
ISBN: 0-7695-1453-7
Xiao Rui, Nanyang Technological University
Chip-Hong Chang, Nanyang Technological University
Thambipillai Srikanthan, Nanyang Technological University
In this paper, we propose a new Gray-Color initialization method for use with the Kohonen's self-organizing feature maps in color image quantization. In our method, the neurons in the competitive layer are initialized in two distinct groups and the input pixels are categorized accordingly. By training the two groups of neurons separately, both the image intensity and color information are better managed for diverse classes of images when the number of neurons is sparse. Compared with the gray scale initialization, our method improves the mean square error of artificial images by 30% on average. The performance gain is achieved with no additional resource and little extra computational effort from the existing SOFM architecture.
Index Terms:
Neural Networks, Self-Organizing Feature Maps, Color Quantization
Citation:
Xiao Rui, Chip-Hong Chang, Thambipillai Srikanthan, "On the Initialization and Training Methods for Kohonen Self-Organizing Feature Maps in Color Image Quantization," delta, pp.321, The First IEEE International Workshop on Electronic Design, Test and Applications (DELTA '02), 2002
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