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One-Dimensional Arimoto Entropy Threshold Segmentation Method Based on Parameters Optimization

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Applied Informatics and Communication (ICAIC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 227))

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Abstract

Applying with the effectiveness of the Arimoto Generalized Entropy function to decision error probability, a One-Dimensional Arimoto entropy threshold segmentation method is proposed. A homogeneity measure is used as the image segmentation quality assessment, in order to obtain the best threshold, an adaptive particle swarm optimization algorithm is used to select the parameters value. The results show, comparing with the fixed parameter value entropy algorithm, our method using the adaptive parameter optimal searching algorithm in the range of (0,1) can get the better segmentation results. For some images, the parameter value is searched in the range of (0,10), the better segmentation results can be obtained.

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Authors and Affiliations

  1. Department of Automation, Xi’an Institute of Post and Telecommunications, Xi’an, Shaanxi, 710061, China

    Hong Zhang

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  1. Hong Zhang
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Editors and Affiliations

  1. Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, China

    Jun Zhang

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© 2011 Springer-Verlag Berlin Heidelberg

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Zhang, H. (2011). One-Dimensional Arimoto Entropy Threshold Segmentation Method Based on Parameters Optimization. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23226-8_74

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  • DOI: https://doi.org/10.1007/978-3-642-23226-8_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23225-1

  • Online ISBN: 978-3-642-23226-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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