Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix

Authors

Haider S. Kaduhm Department of Phesics,College of Education for Pure Sciences, Ibn Al - Haitham, University of Baghdad, Baghdad, Iraq.
Hameed M. Abduljabbar Department of Phesics,College of Education for Pure Sciences, Ibn Al - Haitham, University of Baghdad, Baghdad, Iraq.

DOI:

https://doi.org/10.30526/36.1.2894

Keywords:

Keywords: K-Means, Feature Extraction, Confusion Matrix, Agreement Percent, Class Projection

Abstract

In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every Two cases or two steps (two different angles and for the same number of classes). The agreement percentage between the classification results and the various methods was calculated.

References

Mayada, J. K.; . Emad, K. J.; Decision Tree for Image Classification; Iraqi Commission for Computers and Informatics: Baghdad, 2013.

Latef, A. A. A.; Image Retrieval Based on Coefficient Correlation Index. Ibn AL-Haitham J. Pure Appl. Sci.2017, 25.

Farhan, A.H.; Mohammed Y. Kamil Texture Analysis of Breast Cancer Using Mammogram; Mustansiriyah University /College of Science: Baghdad, 2020.

Zhang, X.; Cui, J.; Wang, W.; Lin, C. A.; Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm. Sensors2017, 17, 1474.

Wirth, M. A.; Texture Analysis. Univ. Guelph Guelph, ON, Canada2004.

Chang, T.; Kuo, C. C.; Texture Analysis and Classification with Tree-Structured Wavelet Transform. IEEE Trans. image Process.1993, 2, 429–441.

Hashim F. A. AL-Bassam; A Texture Analysis System Based on Spatial Frequency and Attributes for Image Classification; University of Baghdad - College of Science Department of Physics: Baghdad, 2019.

Naghashi, V.; Co-Occurrence of Adjacent Sparse Local Ternary Patterns: A Feature Descriptor for Texture and Face Image Retrieval. Optik (Stuttg).2018, 157, 877–889.

Warner, T. A.; Foody, G.M.; Nellis, M.D. The SAGE Handbook of Remote Sensing; Sage Publications, 2009. ISBN 1412936160.

Mohammed, M.A.; Naji, T.A.H.; Abduljabbar, H. M. The Effect of the Activation Functions on the Classification Accuracy of Satellite Image by Artificial Neural Network. Energy Procedia.2019, 157, 164–170.

Akey Sungheetha, D. J. An Efficient Clustering-Classification Method in an Information Gain NRGA-KNN Algorithm for Feature Selection of Micro Array Data. Life Sci. J.2013, 10.

Sharma, A. R.; Beaula, R.; Marikkannu, P.; Sungheetha, A.; Sahana, C. Comparative Study of Distinctive Image Classification Techniques. In Proceedings of the 2016 10th International Conference on Intelligent Systems and Control (ISCO); IEEE, 2016,1–8.

Jain, M.; Tomar, P.S.; Review of Image Classification Methods and Techniques. Int. J. Eng. Res. Technol.2013, 2, 852–858.

Abduljabbar, H.M.; Hatem, A. J.; Al-Jasim, A. A. Desertification Monitoring in the South-West of Iraqi Using Fuzzy Inference System. NeuroQuantology.2020, 18, 1.

Abburu, S.; Golla, S. B.; Satellite Image Classification Methods and Techniques: A Review. Int. J. Comput. Appl.2015, 119.

Mohammed, M. A.; Hatem, A. J.; Change Detection of the Land Cover for Three Decades Using Remote Sensing Data and Geographic Information System. In Proceedings of the AIP Conference Proceedings; AIP Publishing LLC, 2020. 2307, 20029.

Zhang, J.; Tan, T.; Brief Review of Invariant Texture Analysis Methods. Pattern Recognit.2002, 35, 735–747.

Julesz, B.; Caelli, T.; On the Limits of Fourier Decompositions in Visual Texture Perception. Perception.1979, 8, 69–73.

Haralick, R. M.; Statistical and Structural Approaches to Texture. Proc. IEEE1979, 67, 786–804.

Abaas Hussain, L. H.; Correction of Non-Uniform Illumination for Biological Images Using Morphological Operation Assessing with Statistical Features Quality. Ibn AL-Haitham J. Pure Appl. Sci.2017, 29, 81–90.

Hussein, M. A.; Abbas, A. H.; Comparison of Features Extraction Algorithms Used in the Diagnosis of Plant Diseases. Ibn AL-Haitham J. Pure Appl. Sci.2018, 523–538.

Materka, A.; Strzelecki, M. Texture Analysis Methods–a Review. Tech. Univ. lodz, Inst. Electron. COST B11 report, Brussels. 1998,

, 4968.

Suresh, A.; Shunmuganathan, K.L.; Image Texture Classification Using Gray Level Co-Occurrence Matrix Based Statistical Features. Eur. J. Sci. Res.2012, 75, 591–597.

Akhloufi, M.A.; Maldague, X.; Larbi, W.; Ben A New Color-Texture Approach for Industrial Products Inspection. J. Multimed.2008, 3.

Unser, M.; Sum and Difference Histograms for Texture Classification. IEEE Trans. Pattern Anal. Mach. Intell.1986, 118–125.

Rezaei, M.; Saberi, M.; Ershad, S.F. Texture Classification Approach Based on Combination of Random Threshold Vector Technique and Co-Occurrence Matrixes. In Proceedings of the Proceedings of 2011 International Conference on Computer Science and Network Technology; IEEE. 2011, 4, 2303–2306.

Ali, A. H.; Abdulsalam, S. I.; Nema, I. S.; Detection and Segmentation of Ischemic Stroke Using Textural Analysis on Brain CT Images. Int. J. Sci. Eng. Res.2015, 6, 396–400.