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++).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tiwari, D., Tyagi, V.: Dynamic texture recognition: a review. Inf. Syst. Des. Intell. Appl. 434, 365–373 (2016)
Wang, C., Lu, Z., Liao, Q.: Local texture based optical flow for complex brightness variations. In: 21th IEEE International Conference on Image Processing (ICIP), pp. 1972–1976 (2014)
Tiwari, D., Tyagi, V.: Dynamic texture recognition based on completed volume local binary pattern. Multidim. Syst. Signal Process 27(2), 563–575 (2016)
Smith, J.R., Lin, C.-Y., Naphade, M.: Video texture indexing using spatiotemporal wavelets. In: 9th IEEE International Conference on Image Processing (ICIP), pp. 437–440 (2002)
Qiao, Y.L., Song, C.Y., Wang, F.S.: Wavelet-based dynamic texture classification using gumbel distribution. Math. Probl. Eng. 2013(11), 583–603 (2014)
Goncalves, W.N., Machado, B.B., Bruno, O.M.: Spatiotemporal gabor filters: a new method for dynamic texture recognition. SIViP 9(4), 819–830 (2015)
Dubois, S., Peteri, R., Mnard, M.: Characterization and recognition of dynamic textures based on the 2d + t curvelet transform. SIViP 9(4), 819–830 (2015)
Vo, A., Oraintara, S.: A study of relative phase in complex wavelet domain: property, statics and applications in texture image retrieval and segmentation. Sig. Process. Image Commun. 25(1), 28–46 (2010)
Allili, M.S.: Wavelet modeling using finite mixtures of generalized Gaussian distributions: application to texture discrimination and retrieval. IEEE Trans. Image Process. 21(4), 1452 (2012)
Ghanem, B., Ahuja, N.: Maximum margin distance learning for dynamic texture recognition. In: 11th Computer Vision—ECCV 2010, pp. 223–236 (2010)
Derpanis, K.G., Wildes, R.P.: Dynamic texture recognition based on distributions of space time oriented structure. In: 23th Computer Vision and Pattern Recognition (CVPR), pp. 191–198 (2010)
Xu, Y., Huang, S., Ji, H., Fermuller, C.: Scale-space texture description on SIFT-like textons. Comput. Vis. Image Underst. 116, 999–1013 (2012)
Xu, Y., Quan, Y., Ling, H., Ji, H.: Dynamic texture classification using dynamic fractal analysis. In: 13th IEEE International Conference on Computer Vision (ICCV), pp. 1219–1225 (2011)
Chan, A., Vasconcelos, N.: Classifying video with kernel dynamic textures. In: 20th IEEE Conference on Computer Vision & Pattern Recognition, pp. 1–6 (2007)
Tiwari, D., Tyagi, V.: A novel scheme based on local binary pattern for dynamic texture recognition. Comput. Vis. Image Underst. 150, 58–65 (2016)
Wang, Y., Hu, S.: Chaoic features for dynamic textures recognition. Soft. Comput. 20(5), 1977–1989 (2016)
Wang, Y., Hu, S.: Exploiting high level feature for dynamic textures recognition. Neurocomputing 154, 217–224 (2015)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant 61371175.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-5841-8_67
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5840-1
Online ISBN: 978-981-13-5841-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)