The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Dafne Economou - One of the best experts on this subject based on the ideXlab platform.
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improved batch fuzzy learning Vector quantization for image compression
Information Sciences, 2008Co-Authors: George E Tsekouras, Mamalis Antonios, Christos Anagnostopoulos, Damianos Gavalas, Dafne EconomouAbstract:In this paper, we develop a batch fuzzy learning Vector quantization algorithm that attempts to solve certain problems related to the implementation of fuzzy clustering in image compression. The algorithm's structure encompasses two basic components. First, a modified objective function of the fuzzy c-means method is reformulated and then is minimized by means of an iterative gradient-descent procedure. Second, the overall Training procedure is equipped with a systematic strategy for the transition from fuzzy mode, where each Training Vector is assigned to more than one codebook Vectors, to crisp mode, where each Training Vector is assigned to only one codebook Vector. The algorithm is fast and easy to implement. Finally, the simulation results show that the method is efficient and appears to be insensitive to the selection of the fuzziness parameter.
P I Pai - One of the best experts on this subject based on the ideXlab platform.
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fuzzy Vector quantization algorithms and their application in image compression
IEEE Transactions on Image Processing, 1995Co-Authors: N B Karayiannis, P I PaiAbstract:This paper presents the development and evaluation of fuzzy Vector quantization algorithms. These algorithms are designed to achieve the quality of Vector quantizers provided by sophisticated but computationally demanding approaches, while capturing the advantages of the frequently used in practice k-means algorithm, such as speed, simplicity, and conceptual appeal. The uncertainty typically associated with clustering tasks is formulated in this approach by allowing the assignment of each Training Vector to multiple clusters in the early stages of the iterative codebook design process. A Training Vector assignment strategy is also proposed for the transition from the fuzzy mode, where each Training Vector can be assigned to multiple clusters, to the crisp mode, where each Training Vector can be assigned to only one cluster. Such a strategy reduces the dependence of the resulting codebook on the random initial codebook selection. The resulting algorithms are used in image compression based on Vector quantization. This application provides the basis for evaluating the computational efficiency of the proposed algorithms and comparing the quality of the resulting codebook design with that provided by competing techniques. >
George E Tsekouras - One of the best experts on this subject based on the ideXlab platform.
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improved batch fuzzy learning Vector quantization for image compression
Information Sciences, 2008Co-Authors: George E Tsekouras, Mamalis Antonios, Christos Anagnostopoulos, Damianos Gavalas, Dafne EconomouAbstract:In this paper, we develop a batch fuzzy learning Vector quantization algorithm that attempts to solve certain problems related to the implementation of fuzzy clustering in image compression. The algorithm's structure encompasses two basic components. First, a modified objective function of the fuzzy c-means method is reformulated and then is minimized by means of an iterative gradient-descent procedure. Second, the overall Training procedure is equipped with a systematic strategy for the transition from fuzzy mode, where each Training Vector is assigned to more than one codebook Vectors, to crisp mode, where each Training Vector is assigned to only one codebook Vector. The algorithm is fast and easy to implement. Finally, the simulation results show that the method is efficient and appears to be insensitive to the selection of the fuzziness parameter.
Shing-mo Cheng - One of the best experts on this subject based on the ideXlab platform.
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Enhanced initialization method for LBG codebook design algorithm in Vector quantization of images
1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996Co-Authors: Kwok-tung Lo, Shing-mo ChengAbstract:In this paper, a new initialization method is developed for enhancing the LBG codebook design algorithm in image Vector quantization. The proposed method first arranges the Training set data according to three different characteristics of the Training Vector, i.e. mean, variance and shape. A sampling method based on the criterion of maximum error reduction is then developed to select the desired number of representative Vectors in the sorted Training set as the initial codebook for the LBG algorithm. Computer simulations using real images show that the proposed approach outperforms the random guess and the splitting method. With the new approach, a higher quality of boundary preservation and a better local minimum are obtainable through a fewer number of iteration.
N B Karayiannis - One of the best experts on this subject based on the ideXlab platform.
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fuzzy Vector quantization algorithms and their application in image compression
IEEE Transactions on Image Processing, 1995Co-Authors: N B Karayiannis, P I PaiAbstract:This paper presents the development and evaluation of fuzzy Vector quantization algorithms. These algorithms are designed to achieve the quality of Vector quantizers provided by sophisticated but computationally demanding approaches, while capturing the advantages of the frequently used in practice k-means algorithm, such as speed, simplicity, and conceptual appeal. The uncertainty typically associated with clustering tasks is formulated in this approach by allowing the assignment of each Training Vector to multiple clusters in the early stages of the iterative codebook design process. A Training Vector assignment strategy is also proposed for the transition from the fuzzy mode, where each Training Vector can be assigned to multiple clusters, to the crisp mode, where each Training Vector can be assigned to only one cluster. Such a strategy reduces the dependence of the resulting codebook on the random initial codebook selection. The resulting algorithms are used in image compression based on Vector quantization. This application provides the basis for evaluating the computational efficiency of the proposed algorithms and comparing the quality of the resulting codebook design with that provided by competing techniques. >