Speedup of Color Palette Indexing in Self-Organization of Kohonen Feature Map

• Main Authors: Jyun-Pin Wang, 汪均嬪
• Other Authors: Kuo-Liang Chung
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/30896519415654495830

碩士 === 國立臺灣科技大學 === 資訊工程系 === 97 === Based on the self-organization of Kohonen feature map (SOFM), recently, Pei et al. presented an efficient color palette indexing method to construct a color table for compression. Taking the palette indexing method as a representative, this thesis presents two new strategies, the pruning-based search strategy and the lookup table (LUT)-based update strategy, to speed up the learning process in the SOFM. The proposed search strategy is used to speed up the process for finding the winning neuron in each iteration; the proposed LUT-based update strategy is used to speed up the lateral update interaction between the winning neuron and its neighboring neurons in the SOFM. Based on four typical testing images, experimental results demonstrate that our proposed two strategies have 35% execution-time improvement ratio in average. The practical improvement ratio is very close to that in the theoretical analysis.