Combinations of Feature Descriptors for Texture Image Classification


Texture recognition and classification is a widely applicable task in computer vision. A key stage in performing this task is feature extraction, which identifies sets of features that describe the visual texture of an image. Many descriptors can be used to perform texture classification; among the more common of these are the grey level co-occurrence matrix, Gabor wavelets, steerable pyramids and SIFT. We analyse and compare the effectiveness of these methods on the Brodatz, UIUCTex and KTH-TIPS texture image datasets. The efficacy of the descriptors is evaluated both in isolation and by combining several of them by means of machine learning approaches such as Bayesian networks, support vector machines, and nearest-neighbour approaches. We demonstrate that using a combination of features improves reliability over using a single feature type when multiple datasets are to be classified. We determine optimal combinations for each dataset and achieve high classification rates, demonstrating that relatively simple descriptors can be made to perform close to the very best published results. We also demonstrate the importance of selecting the optimal descriptor set and analysis techniques for a given dataset.

Share and Cite:

Barley, A. and Town, C. (2014) Combinations of Feature Descriptors for Texture Image Classification. Journal of Data Analysis and Information Processing, 2, 67-76. doi: 10.4236/jdaip.2014.23009.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Hossain, S. and Serikawa, S. (2012) Features for Texture Analysis. SICE Annual Conference, 1739-1744.
[2] Roslan, R. and Jamil, N. (2012) Texture Feature Extraction Using 2-D Gabor Filters. International Symposium on Computer Applications and Industrial Electronics, 3-4 December 2012, Kota Kinabalu, 173-178.
[3] Reddy, T. and Kumaravel, N. (2012) A Comparison of Wavelet, Curvelet and Contourlet Based Texture Classification Algorithms for Characterization of Bone Quality in Dental CT. 2011 International Conference on Environmental, Biomedical and Biotechnology, 16, 60-65.
[4] Randen, T. and Hus?y, J. (1999) Filtering for Texture Classification: A Comparative Study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 291-310.
[5] Sharma, M., et al. (1980) Evaluation of Texture Methods for Image Analysis. Proceedings of the 7th Australian and New Zealand Intelligent Information Systems Conference.
[6] Kumar, V., et al. (2007) An Innovative Technique of Texture Classification and Comparison Based on Long Linear Patterns. Journal of Computer Science, 633-638.
[7] Sumana, I., et al. (2012) Comparison of Curvelet and Wavelet Texture Features for Content Based Image Retrieval. IEEE International Conference on Multimedia and Expo, 290-295.
[8] Zhang, J., et al. (2012) Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. International Journal of Computer Vision, 1739-1744.
[9] Janney, P. and Geers, G. (2010) Texture Classification Using Invariant Features of Local Textures. IET Image Pro- cessing, 4, 158-171.
[10] Do, T., Aikala, A. and Saarela, O. (2012) Framework for Texture Classification and Retrieval Using Scale Invariant Feature Transform. Ninth International Joint Conference on Computer Science and Software Engineering, 30 May-1 June 2012, Bangkok, 289-293.
[11] Yang, Y. and Newsam, S. (2008) Comparing SIFT Descriptors and Gabor Texture Features for Classification of Remote Sensed Imagery. IEEE International Conference on Image Processing, San Diego, 12-15 October 2008, 1852- 1855.
[12] Mustafa, M., Taib, M.N., Murat, Z.H. and Hamid, N.H.A. (2010) GLCM Texture Classification for EEG Spectrogram Image. IEEE EMBS Conference on Biomedical Engineering & Sciences, Kuala Lumpur, 30 November-2 December 2010, 373-376.
[13] Do, M. and Vetterli, M. (2002) Rotation Invariant Texture Characterization and Retrieval Using Steerable Wavelet- Domain Hidden Markov Models. IEEE Transactions on Multimedia, 4, 517-527.
[14] Burt, P. and Adelson, E. (1983) The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communications, 31, 532-540.
[15] Greenspan, H., Belongie, S., Goodman, R., Perona, P., Rakshit, S. and Anderson, C.H. (1994) Overcomplete Steerable Pyramid Filters and Rotation Invariance. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, 21-23 June 1994, 222-228.
[16] Lowe, D. (2004) Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60, 91-110.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.