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Dynamic Texture Classification with Relative Phase Information in the Complex Wavelet Domain

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Genetic and Evolutionary Computing (ICGEC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 834))

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Abstract

In recent years, dynamic texture classification has caused widespread concern in the image sequence analysis field. We propose a new method of combining relative phase information of dynamic texture in the complex wavelet domain with probability distribution models for dynamic classification in this paper. Instead of using only real or magnitude information of dynamic texture, relative phase information is an effective complementary measure for dynamic texture classification. Firstly, the finite mixtures of Von Mises distributions (MoVMD) and corresponding parameter estimation method based on expectation-maximization (EM) algorithm are introduced. Subsequently, the dynamic texture features based on MoVMD model for dynamic texture classification are proposed. Besides, the relative phase information of dynamic texture is modeled with MoVMDs after decomposing dynamic texture with the dual-tree complex wavelet transform (DT-CWT). Finally, the variational approximation between different dynamic textures is measured using the Kuller-Leibler divergence (KLD) variational approximation. The effectiveness of the proposed method is verified by experimental evaluation of two popular benchmark texture databases (UCLA and DynTex++).

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant 61371175.

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Authors and Affiliations

  1. 724 Research Institute, Nanjing, 211153, China

    Qiufei Liu, Bingye Liu & Jiayuan Liu

  2. Harbin Engineering University, Harbin, 150001, China

    Xiaoyong Men & Yulong Qiao

  3. Zhoukou Normal University, Zhoukou, 466001, China

    Qiuxia Liu

Authors
  1. Qiufei Liu
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  2. Xiaoyong Men
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  3. Yulong Qiao
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  4. Bingye Liu
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  5. Jiayuan Liu
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  6. Qiuxia Liu
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Corresponding author

Correspondence to Qiufei Liu .

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Editors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen, China

    Jerry Chun-Wei Lin

  3. Changzhou College of Information Technology, Changzhou, Jiangsu, China

    Bixia Sui

  4. College of Smart Living and Management, Tajen University, Pingtung, Taiwan

    Shih-Pang Tseng

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Liu, Q., Men, X., Qiao, Y., Liu, B., Liu, J., Liu, Q. (2019). Dynamic Texture Classification with Relative Phase Information in the Complex Wavelet Domain. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_67

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