Affinity Propagation for the Context Quantization

Min Chen; Jian Hua  Chen; Mo Hai Guo

doi:10.4028/www.scientific.net/AMR.791-793.1533

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 791-793Affinity Propagation for the Context Quantization

Affinity Propagation for the Context Quantization

Abstract:

In this paper, the context quantization for I-ary sources based on the affinity propagation algorithm is presented. In purpose of finding the optimal number of classes, the increment of the adaptive code length is suggested to be the similarity measure between two conditional probability distributions, by which the similarity matrix is constructed as the input of the affinity propagation algorithm. After the given number of iterations, the optimal quantizer with the optimal number of classes is achieved and the adaptive code length is minimized at the same time. The simulations indicate that the proposed algorithm produces results that are better than the results obtained by the minimum conditional entropy context quantization implemented by K-means with lower computational complexity.

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Periodical:

Advanced Materials Research (Volumes 791-793)

Pages:

1533-1536

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.791-793.1533

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Online since:

September 2013

Authors:

Min Chen, Jian Hua Chen, Mo Hai Guo

Keywords:

Affinity Propagation, Context Quantization, Increment of Adaptive Code Length

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