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A Comparative Study on Encoding Methods of Local Binary Patterns for Image Segmentation

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Advances in Smart Vehicular Technology, Transportation, Communication and Applications (VTCA 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 128))

Abstract

Clustering for image segmentation is widely used in image recognition. Euclidean distances among pixels are calculated for discriminating the belongingness to clusters. However, Euclidean distances vary in different feature representation that may affect the image clustering results. This study discusses the abovementioned problem wherein the local binary pattern (LBP) is employed as features for clustering. Four popular LBP encoding schemes are discussed and compared. Tests on medical image segmentation using the fuzzy c-means (FCM) clustering method are conducted. Experimental results show that with a proper LBP encoding scheme, a better clustering result may be obtained.

This work was supported in part by the STSP AI Robot Project under grant B106002 and the Ministry of Science and Technology, Taiwan, under grant MOST 107-2221-E-390-022.

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References

  1. Yi, L., Zhang, G., Wu, Z.: A scale-synthesis method for high spatial resolution remote sensing image segmentation. IEEE Trans. Geosci. Remote Sens. 50, 4062–4070 (2012)

    Article  Google Scholar 

  2. Chen, J., Zheng, H., Lin, X., Wu, Y., Su, M.: A novel image segmentation method based on fast density clustering algorithm. Eng. Appl. Artif. Intell. 73, 92–110 (2018)

    Article  Google Scholar 

  3. Forgy, E.: Cluster analysis of multivariate data: efficiency versus interpretability of classification. Biometrics 21(3), 768–769 (1965)

    Google Scholar 

  4. Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)

    Article  MathSciNet  Google Scholar 

  5. Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  6. Parida, P., Bhoi, N.: Fuzzy clustering based transition region extraction for image segmentation. Eng. Sci. Technol. Int. J. 21(4), 547–563 (2018)

    Article  Google Scholar 

  7. Abbasi, S., Tajeripour, F.: Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219, 526–535 (2017)

    Article  Google Scholar 

  8. Awad, M., Chehdi, K., Nasri, A.: Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means. IET Image Proc. 3(2), 52–62 (2009)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Road, Nan-Tzu, Kaohsiung, 811, Taiwan

    Chih-Hung Wu, Chih-Chin Lai, Hsien-Jen Lo & Po-Sen Wang

Authors
  1. Chih-Hung Wu
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  2. Chih-Chin Lai
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  3. Hsien-Jen Lo
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  4. Po-Sen Wang
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Corresponding author

Correspondence to Chih-Hung Wu .

Editor information

Editors and Affiliations

  1. College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian, China

    Yong Zhao

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Tsu-Yang Wu

  3. School of Transportation, Fujian University of Technology, Fuzhou, Fujian, China

    Tang-Hsien Chang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  5. University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Wu, CH., Lai, CC., Lo, HJ., Wang, PS. (2019). A Comparative Study on Encoding Methods of Local Binary Patterns for Image Segmentation. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_33

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