Abstract
In order to recognize Chinese sign language more accurately, we proposed an efficient method using gray-level co-occurrence matrix (GLCM) and parameter-optimized medium Gaussian support vector machine (MGSVM). First, sign language images were acquired by digital camera or picked from video as keyframes, and then the hand shapes were segmented from background. Second, each image was resized to N × N size and converted into gray-level image. The number of intensity values in grayscale image was reduced from 256 to 8, and gray-level co-occurrence matrix was created. Third, the extracted and reduced features were sent to MGSVM; meanwhile, the classification was performed on a tenfold cross-validation. The experimental results of the 450 isolated Chinese sign language images from the 30 categories demonstrated that the GLCM–MGSVM achieved a classification accuracy of 85.3%, which was much higher than GLCM-DT (decision tree). Therefore, the GLCM-MGSVM was seen to be effective in classifying Chinese sign language.
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
Zhan, T.: Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning. Prog. Electromagnet. Res. 156, 105–133 (2016)
Wu, L.: A hybrid method for MRI brain image classification. Expert Syst. Appl. 38(8), 10049–10053 (2011)
Metaxas, D., Dilsizian, M., Neidle, C.: Scalable ASL sign recognition using model-based machine learning and linguistically annotated corpora. In: Language Resources and Evaluation (2018)
Pan, T.-Y., Lo, L.-Y, Yeh, C.-W., et al.: Sign language recognition in complex background scene based on adaptive skin colour modelling and support vector machine. Int. J. Big Data Intell 5, 1–2 (2018)
Kishore, P.V.V., Prasad, M.V.D., Prasad, C.R., Rahul, R.: 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN. In: International Conference on Signal Processing and Communication Engineering Systems (2015)
Lei, L., Dashun, Q.: Design of data-glove and Chinese sign language recognition system based on ARM9. In: 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (2015)
Pigou, L., Dieleman, S., Kindermans, P.J., Schrauwen, B.: Sign language recognition using convolutional neural networks. In: European Conference on Computer Vision: 572–578 (2014)
Geng, L., Ma, X., Xue, B., et al.: Combining features for Chinese sign language recognition with Kinect. In: 11th IEEE International Conference on Control & Automation (ICCA) (2014)
Chuan, C.H., Regina, E., Guardino, C.: American sign language recognition using leap motion sensor. In: 13th International Conference on Machine Learning and Applications (2014)
Ruiliang Su, X.C., Cao, Shuai, et al.: Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors. J. Sens. 16(1), 100 (2016)
Ahmed, W., Chanda, K., Mitra, S.: Vision based hand gesture recognition using dynamic time warping for Indian sign language. In: International Conference on Information Science (ICIS) (2016)
Pan, C.: Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. J. Comput. Sci. 28, 1–10 (2018). https://doi.org/10.1016/j.jocs.2018.07.003
Pan, C.: Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. J. Comput. Sci. 27, 57–68 (2018). https://doi.org/10.1016/j.jocs.2018.05.005
Tang, C.: Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform. Multimedia Tools Appl. 77(17), 22821–22839 (2018). https://doi.org/10.1007/s11042-018-5765-3
Lv, Y.D.: Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. J. Med Syst. 42(1), 2 (2018)
Muhammad, K.: Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools Appl. (2017). https://doi.org/10.1007/s11042-017-5243-3
Wu, J.: Fruit classification by biogeography-based optimization and feedforward neural network. Expert Syst. 33(3), 239–253 (2016). https://doi.org/10.1111/exsy.12146
Wei, L.: Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 17(8), 5711–5728 (2015). https://doi.org/10.3390/e17085711
Ji, G.: Fruit classification using computer vision and feedforward neural network. J. Food Eng. 143, 167–177 (2014). https://doi.org/10.1016/j.jfoodeng.2014.07.001
Zhao, G.: Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and jaya algorithm. Multimedia Tools Appl. 77(17), 22629–22648 (2018). https://doi.org/10.1007/s11042-017-5023-0
Lu, S.: Pathological brain detection in magnetic resonance imaging using combined features and improved extreme learning machines. J. Medical Imaging Health Inform. 8, 1486–1490 (2018)
Muhammad, K.: Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension. J Ambient Intell. Humanized Comput. (2017). https://doi.org/10.1007/s12652-017-0639-5
Mao, C., Huang, S., Li, X., Ye, Z.: Chinese sign language recognition with sequence to sequence learning. In: CCF Chinese Conference on Computer Vision: 180–191 (2017)
Mellisa Pratiwi, A., Harefa, Jeklin, Nanda, Sakka: Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. Procedia Comput. Sci. 59, 83–91 (2015)
Matlab: (2018). http://matlab.izmiran.ru/help/toolbox/images/enhanc15.html
Wei, G.: A new classifier for polarimetric SAR images. Prog. Electromagnet. Res. 94, 83–104 (2009)
Naggaz, N.: Remote-sensing image classification based on an improved probabilistic neural network. Sensors 9(9), 7516–7539 (2009)
Lu, H.M.: Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4, 8375–8385 (2016). https://doi.org/10.1109/ACCESS.2016.2628407
Gorriz, J.M., Ramírez, J.: Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front Comput. Neurosci. 10 (2016). Article ID: 160. https://doi.org/10.3389/fncom.2016.00106
Dong, Z.: Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog. Electromagnet. Res. 144, 171–184 (2014). https://doi.org/10.2528/PIER13121310
Zhang, Y.: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl.-Based Syst. 64, 22–31 (2014)
Zhou, X.-X.: Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9), 861–871 (2016). https://doi.org/10.1177/0037549716666962
Wylie, C.E.S.D., Verheyen, K.L.P., et al.: Decision-tree analysis of clinical data to aid diagnostic reasoning for equine laminitis: a cross-sectional study. Vet. Rec. 178, 8 (2016)
Yang, J.: Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy 17(4), 1795–1813 (2015). https://doi.org/10.3390/e17041795
Liu, A.: Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. J. Medical Imaging Health Inform. 5(7), 1395–1403 (2015). https://doi.org/10.1166/jmihi.2015.1542
Liu, G.: Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus 4(1) (2015). Article ID: 716
Chen, S., Yang, J.-F., Phillips, P.: Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int. J. Imaging Syst. Technol. 25(4), 317–327 (2015). https://doi.org/10.1002/ima.22144
Zhou, X.-X., Sheng, H.: Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. Simulation 92(9), 827–837 (2016). https://doi.org/10.1177/0037549716629227
Acknowledgements
This work was supported by Jiangsu Overseas Visiting Scholar Program for University Prominent Young and Middle-aged Teachers and Presidents of China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, X. (2020). Isolated Chinese Sign Language Recognition Using Gray-Level Co-occurrence Matrix and Parameter-Optimized Medium Gaussian Support Vector Machine. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1014. Springer, Singapore. https://doi.org/10.1007/978-981-13-9920-6_19
Download citation
DOI: https://doi.org/10.1007/978-981-13-9920-6_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9919-0
Online ISBN: 978-981-13-9920-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)