Context Quantization Based on the Modified K-Means Clustering

Min Chen; Fu Yan Wang

doi:10.4028/www.scientific.net/AMR.756-759.4068

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Context Quantization Based on the Modified K-Means...

Context Quantization Based on the Modified K-Means Clustering

Abstract:

The context quantization forsource based on the modified K-means clustering algorithm is present in this paper. In this algorithm, the adaptive complementary relative entropy between two conditional probability distributions, which is used as the distance measure for K-means instead, is formulated to describe the similarity of these two probability distributions. The rules of the initialized centers chosen for K-means are also discussed. The proposed algorithm will traverse all possible number of the classes to search the optimal one which is corresponding to the shortest adaptive code length. Then the optimal context quantizer is achieved rapidly and the adaptive code length is minimized at the same time. Simulations indicate that the proposed algorithm produces better coding result than the result of other algorithm.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

4068-4072

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.4068

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Online since:

September 2013

Authors:

Min Chen, Fu Yan Wang

Keywords:

Context Quantization, K-Mean, Modeling Context

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