Abstract
Texture is one of the important characteristics of an image, and its features can be used to identify specific region of interest from the image. Texture features can be extracted for any kind of images such as RGB, monochrome, aerial, and satellite images. This chapter describes an approach for texture image classification based on Grey-Level Co-occurrence Matrix (GLCM) features and machine learning algorithm such as support vector machine. Ten different GLCM features were extracted and fed as input to support vector machine for classification. The proposed method is trained and tested with dataset collected from Center for Image Analysis, Swedish University. In recent days, machine learning (ML) methods were highly used to mimic the complex mathematical expressions of texture features. The main objective of this chapter is to demonstrate the state of the art of ML models in texture image prediction and to give insight into the most suitable models.
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
References
Shanthi, T., Sabeenian, R. S., Manju, K., Paramasivam, M. E., Dinesh, P. M., & Anand, R. (2021). Fundus image classification using hybridized GLCM features and wavelet features. ICTACT Journal of Image and Video Processing, 11(03), 2345–2348.
Veni, S., Anand, R., & Vivek, D. (2020). Driver assistance through geo-fencing, sign board detection and reporting using android smartphone. In: K. Das, J. Bansal, K. Deep, A. Nagar, P. Pathipooranam, & R. Naidu (Eds.), Soft computing for problem solving. Advances in Intelligent Systems and Computing (Vol. 1057). Singapore: Springer.
Kylberg, G. The Kylberg Texture Dataset v. 1.0, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, External report (Blue series) No. 35. Available online at: http://www.cb.uu.se/gustaf/texture/
Anand, R., Veni, S., & Aravinth, J. (2016) An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE.
Sabeenian, R. S., & Palanisamy, V. (2009). Texture-based medical image classification of computed tomography images using MRCSF. International Journal of Medical Engineering and Informatics, 1(4), 459.
Sabeenian, R. S., & Palanisamy, V. (2008). Comparison of efficiency for texture image classification using MRMRF and GLCM techniques. Published in International Journal of Computers Information Technology and Engineering (IJCITAE), 2(2), 87–93.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621.
Varma, M., & Zisserman, A. (2005). A statistical approach to texture classification from single images. International Journal of Computer Vision, 62(1–2), 61–81.
Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.
Shanthi, T., Sabeenian, R. S. (2019). Modified AlexNet architecture for classification of diabetic retinopathy images. Computers and Electrical Engineering, 76, 56–64.
Sabeenian, R. S., Paramasivam, M. E., Selvan, P., Paul, E., Dinesh, P. M., Shanthi, T., Manju, K., & Anand, R. (2021). Gold tree sorting and classification using support vector machine classifier. In Advances in Machine Learning and Computational Intelligence (pp. 413–422). Singapore: Springer.
Shobana, R. A., & Shanthi, D. T. (2018). GLCM based plant leaf disease detection using multiclass SVM. International Journal For Research & Development In Technology, 10(2), 47–51.
Scholkopf, B., & Smola, A. J. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. In Advances in Neural Information Processing Systems (pp. 368–374).
Anand, R., Shanthi, T., Nithish, M. S., & Lakshman, S. (2020). Face recognition and classification using GoogLeNET architecture. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., & Naidu, R. (Eds.), Soft computing for problem solving. Advances in Intelligent Systems and Computing (Vol. 1048). Singapore: Springer.
Shanthi, T., Sabeenian, R. S., & Anand, R. (2020). Automatic diagnosis of skin diseases using convolution neural network. Microprocessors and Microsystems, 76, 103074.
Hall-Beyer, M. (2000). GLCM texture: A tutorial. In National Council on Geographic Information and Analysis Remote Sensing Core Curriculum 3.
Shanthi, T., Anand, R., Annapoorani, S., & Birundha, N. (2023). Analysis of phonocardiogram signal using deep learning. In D. Gupta, A. Khanna, S. Bhattacharyya, A. E. Hassanien, S. Anand, & A. Jaiswal (Eds.), International conference on innovative computing and communications (Lecture notes in networks and systems) (Vol. 471). Springer. https://doi.org/10.1007/978-981-19-2535-1_48
Kandasamy, S. K., Maheswaran, S., Karuppusamy, S. A., Indra, J., Anand, R., Rega, P., & Kathiresan, K. (2022). Design and fabrication of flexible Nanoantenna-based sensor using graphene-coated carbon cloth. Advances in Materials Science & Engineering.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Anand, R., Shanthi, T., Sabeenian, R.S., Veni, S. (2023). GLCM Feature-Based Texture Image Classification Using Machine Learning Algorithms. In: Kumar, B.V., Sivakumar, P., Surendiran, B., Ding, J. (eds) Smart Computer Vision. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-20541-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-20541-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20540-8
Online ISBN: 978-3-031-20541-5
eBook Packages: EngineeringEngineering (R0)