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Self-Organizing Neural Network Domain Classification for Fractal Image Coding

Self-Organizing Neural Network DomainClassification for Fractal Image Coding
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摘要 This paper presents a scheme for improving encoding time for fractal image compression. The approachcombines feature extraction with domain classification using a selforganizing neural network. Feature extractionreduces the dimensionalics of the problem and enables the neural network to be trained on an image separate fromthe test image. The seaorganizing network introduces a neighborhood topology for classytcation, and alsoeliminates the need to specify a prior set of appropriate image classes. The network organizes itself according to thedistribution of the image features observed during the training. The paper presents results showing that thisclassification approach can reduce encoding time by two orders of magnitude while maintaining comparableaccuracy and compression performance. This paper presents a scheme for improving encoding time for fractal image compression. The approachcombines feature extraction with domain classification using a selforganizing neural network. Feature extractionreduces the dimensionalics of the problem and enables the neural network to be trained on an image separate fromthe test image. The seaorganizing network introduces a neighborhood topology for classytcation, and alsoeliminates the need to specify a prior set of appropriate image classes. The network organizes itself according to thedistribution of the image features observed during the training. The paper presents results showing that thisclassification approach can reduce encoding time by two orders of magnitude while maintaining comparableaccuracy and compression performance.
出处 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 1999年第2期54-58,共5页 中国邮电高校学报(英文版)
关键词 neural networks image compression unsupervised learning fractals neural networks image compression unsupervised learning fractals
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