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1 April 1997 Entropy-constrained learning vector quantization algorithms and their application in image compression
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Abstract
This paper presents entropy constrained fuzzy clustering and learning vector quantization algorithms and their application in image compression. Entropy constrained fuzzy clustering (ECFC) algorithms were developed by minimizing an objective function incorporating the fuzzy partition entropy and the average distortion between the feature vectors, which represent the image data, and the prototypes, which represent the codevectors or codewords. The reformulation of fuzzy c-means (FCM) algorithms provided the basis for the development of fuzzy learning vector quantization (FLVQ) algorithms and essentially established a link between clustering and learning vector quantization. Minimization of the reformulation function that corresponds to ECFC algorithms using gradient descent results in entropy constrained learning vector quantization (ECLVQ) algorithms. These algorithms allow the gradual transition from a maximally fuzzy partition to a nearly crisp partition of the feature vectors during the learning process. This paper presents two alternative implementations of the proposed algorithms, which differ in terms of the strategy employed for updating the prototypes during learning. The proposed algorithms are tested and evaluated on the design of codebooks used for image data compression.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nicolaos B. Karayiannis "Entropy-constrained learning vector quantization algorithms and their application in image compression", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); https://doi.org/10.1117/12.269766
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