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A Novel MRF-Based Image Segmentation Approach

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Advances in Image and Graphics Technologies (IGTA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 525))

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Abstract

This paper confirms the utility of Ohta color space, GLOM and MRF model to enhance the accuracy of segmentation of color textured images. The statistical properties of color textured images in Ohta color space are explored by means of GLOM and the segmentation is done by contextual modeling of the data through MRF modeling. The Haralick feature Mean at IPD 1, as optimized with this approach, appears to be the best textural feature to improve interclass discrimination. The results obtained by our tests are compared with those of MRF modeling in RGB color space and our method found to be the better choice.

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Author information

Authors and Affiliations

  1. Network center of Shenyang Jianzhu University, Shenyang, 100168, China

    Wei Liu, Feng Yu & Chunyang Gao

Authors
  1. Wei Liu
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  2. Feng Yu
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  3. Chunyang Gao
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Corresponding author

Correspondence to Wei Liu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences Instute of Automation, Beijing, China

    Tieniu Tan

  2. Beijing Jiaotong University, Beijing, China

    Qiuqi Ruan

  3. Tsinghua University, Beijing, China

    Shengjin Wang

  4. Tsinghua University, Beijing, China

    Huimin Ma

  5. Chinese Academy of Sciences, Beijing, China

    Kaichang Di

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© 2015 Springer-Verlag Berlin Heidelberg

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Liu, W., Yu, F., Gao, C. (2015). A Novel MRF-Based Image Segmentation Approach. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_18

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  • DOI: https://doi.org/10.1007/978-3-662-47791-5_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47790-8

  • Online ISBN: 978-3-662-47791-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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