Digital Image Object Detection with GLCM Multi-Degrees and Ensemble Learning
Abstract
Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours (KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degrees and the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degrees is 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracy for detecting objects experiencing rotation
Downloads
References
G. Saleem, M. Akhtar, N. Ahmed, and W. S. Qureshi, ‘Automated analysis of visual leaf shape features for plant classification’, Comput. Electron. Agric., vol. 157, no. December 2018, pp. 270–280, 2019, doi: 10.1016/j.compag.2018.12.038.
M. Hussain, D. Koundal, and J. Manhas, ‘Deep learning-based diagnosis of disc degenerative diseases using MRI: A comprehensive review’, Comput. Electr. Eng., vol. 105, no. January 2022, p. 108524, 2023, doi: 10.1016/j.compeleceng.2022.108524.
M. N. Amin, R. Kamal, A. Farouk, M. Gomaa, M. A. Rushdi, and A. M. Mahmoud, ‘An efficient hybrid computer-aided breast cancer diagnosis system with wavelet packet transform and synthetically-generated contrast-enhanced spectral mammography images’, Biomed. Signal Process. Control, vol. 85, no. March, p. 104808, 2023, doi: 10.1016/j.bspc.2023.104808.
M. Maqsood et al., ‘An autonomous decision-making framework for gait recognition systems against adversarial attack using reinforcement learning’, ISA Trans., vol. 132, pp. 80–93, 2023, doi: 10.1016/j.isatra.2022.11.016.
N. Aherrahrou and H. Tairi, ‘A novel cancelable finger vein templates based on LDM and RetinexGan’, Pattern Recognit., vol. 142, p. 109643, 2023, doi: 10.1016/j.patcog.2023.109643.
C. De Souza Rocha Junior et al., ‘IoT technology proposal for multi-adaptative sensing integrated into data science and analytics scenarios’, Procedia Comput. Sci., vol. 214, no. C, pp. 108–116, 2022, doi: 10.1016/j.procs.2022.11.155.
A. H. Rangkuti, A. Harjoko, and A. Putra, ‘A Novel Reliable Approach for Image Batik Classification That Invariant with Scale and Rotation Using MU2ECS-LBP Algorithm’, Procedia Comput. Sci., vol. 179, no. 2019, pp. 863–870, 2021, doi: 10.1016/j.procs.2021.01.075.
J. Chaki and M. Woźniak, ‘A deep learning based four-fold approach to classify brain MRI: BTSCNet’, Biomed. Signal Process. Control, vol. 85, no. April, 2023, doi: 10.1016/j.bspc.2023.104902.
S. Khan and M. Narvekar, ‘Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment’, J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 3506–3516, 2022, doi: 10.1016/j.jksuci.2020.09.006.
Z. Ullah, L. Qi, A. Hasan, and M. Asim, ‘Improved Deep CNN-based Two Stream Super Resolution and Hybrid Deep Model-based Facial Emotion Recognition’, Eng. Appl. Artif. Intell., vol. 116, no. October, p. 105486, 2022, doi: 10.1016/j.engappai.2022.105486.
M. Mustafa, M. N. Taib, Z. H. Murat, N. Sulaiman, and S. A. M. Aris, ‘The Analysis of EEG Spectrogram Image for Brainwave Balancing Application Using ANN’, 2011 UkSim 13th Int. Conf. Comput. Model. Simul., pp. 64–68, Mar. 2011, doi: 10.1109/UKSIM.2011.22.
S. Vidivelli and S. Sathiya Devi, ‘Breast cancer detection model using fuzzy entropy segmentation and ensemble classification’, Biomed. Signal Process. Control, vol. 80, no. P1, p. 104236, 2023, doi: 10.1016/j.bspc.2022.104236.
D. Rahadiyan, S. Hartati, Wahyono, and A. P. Nugroho, ‘Feature aggregation for nutrient deficiency identification in chili based on machine learning’, Artif. Intell. Agric., vol. 8, pp. 77–90, 2023, doi: 10.1016/j.aiia.2023.04.001.
T. S. Prakash, A. Siva Kumar, C. R. B. Durai, and S. Ashok, ‘Enhanced Elman spike Neural network optimized with flamingo search optimization algorithm espoused lung cancer classification from CT images’, Biomed. Signal Process. Control, vol. 84, no. March, p. 104948, 2023, doi: 10.1016/j.bspc.2023.104948.
C. Nyasulu et al., ‘A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features’, Heliyon, vol. 9, no. 11, p. e21697, 2023, doi: 10.1016/j.heliyon.2023.e21697.
T. Zhang, J. Zheng, and Y. Zou, ‘Weighted voting ensemble method for predicting workpiece imaging dimensional deviation based on monocular vision systems’, Opt. Laser Technol., vol. 159, no. October 2022, p. 109012, 2023, doi: 10.1016/j.optlastec.2022.109012.
R. Islam, M. I. Sayed, S. Saha, M. J. Hossain, and M. A. Masud, ‘Android malware classification using optimum feature selection and ensemble machine learning’, Internet Things Cyber-Physical Syst., vol. 3, no. October 2022, pp. 100–111, 2023, doi: 10.1016/j.iotcps.2023.03.001.
M. A. Shehab and N. Kahraman, ‘A weighted voting ensemble of efficient regularized extreme learning machine’, Comput. Electr. Eng., vol. 85, 2020, doi: 10.1016/j.compeleceng.2020.106639.
R. Sulaiman, N. H. Azeman, M. H. H. Mokhtar, N. N. Mobarak, M. H. Abu Bakar, and A. A. A. Bakar, ‘Hybrid ensemble-based machine learning model for predicting phosphorus concentrations in hydroponic solution’, Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 304, no. May 2023, 2024, doi: 10.1016/j.saa.2023.123327.
G. Yuan, L. Lu, and X. Zhou, ‘Feature selection using a sinusoidal sequence combined with mutual information’, Eng. Appl. Artif. Intell., vol. 126, no. September, 2023, doi: 10.1016/j.engappai.2023.107168.
Y. Li, H. S. Abdel-Khalik, A. Al Rashdan, and J. Farber, ‘Feature extraction for subtle anomaly detection using semi-supervised learning’, Ann. Nucl. Energy, vol. 181, no. October 2022, p. 109503, 2023, doi: 10.1016/j.anucene.2022.109503.
I. Made Agus Wirawan, R. Wardoyo, and D. Lelono, ‘The challenges of emotion recognition methods based on electroencephalogram signals: A literature review’, Int. J. Electr. Comput. Eng., vol. 12, no. 2, pp. 1508–1519, 2022, doi: 10.11591/ijece.v12i2.pp1508-1519.
Z. H. Kok, A. R. Mohamed Shariff, M. S. M. Alfatni, and S. Khairunniza-Bejo, ‘Support Vector Machine in Precision Agriculture: A review’, Comput. Electron. Agric., vol. 191, no. November, p. 106546, 2021, doi: 10.1016/j.compag.2021.106546.
M. T. Ho, P. Mantello, and M. T. Ho, ‘An analytical framework for studying attitude towards emotional AI: The three-pronged approach’, MethodsX, vol. 10, no. March, 2023, doi: 10.1016/j.mex.2023.102149.
U. Haider, M. Waqas, M. Hanif, H. Alasmary, and S. M. Qaisar, ‘Network load prediction and anomaly detection using ensemble learning in 5G cellular networks’, Comput. Commun., vol. 197, no. October 2022, pp. 141–150, 2023, doi: 10.1016/j.comcom.2022.10.017.
D. Yadav et al., ‘Microaneurysm detection using color locus detection method’, Meas. J. Int. Meas. Confed., vol. 176, no. January, p. 109084, 2021, doi: 10.1016/j.measurement.2021.109084.
Y. Gu, Y. Li, Y. Yang, B. Xiao, D. Zhang, and Z. Bao, ‘Classification pattern of lacustrine carbonate diagenetic facies and logging-based data-driven prediction via a generalized and robust ensemble learning: A demonstration of pre-salt profile, santos basin’, Geoenergy Sci. Eng., vol. 223, no. December 2022, p. 211543, 2023, doi: 10.1016/j.geoen.2023.211543.
O. Bukhari, P. Agarwal, D. Koundal, and S. Zafar, ‘Anomaly detection using ensemble techniques for boosting the security of intrusion detection system’, Procedia Comput. Sci., vol. 218, pp. 1003–1013, 2023, doi: 10.1016/j.procs.2023.01.080.
P. Bhupathi, S. Prabu, and A. P. I. Goh, ‘Artificial Intelligence-Enabled Knowledge Management Using a Multidimensional Analytical Framework of Visualizations’, Int. J. Cogn. Comput. Eng., vol. 4, no. July, pp. 240–247, 2023, doi: 10.1016/j.ijcce.2023.06.003.
M. Kaya and M. Eris, ‘D3SENet: A hybrid deep feature extraction network for Covid-19 classification using chest X-ray images’, Biomed. Signal Process. Control, vol. 82, no. August 2022, p. 104559, 2023, doi: 10.1016/j.bspc.2022.104559.
D. Gupta, J. Arora, U. Agrawal, A. Khanna, and V. H. C. de Albuquerque, ‘Optimized Binary Bat algorithm for classification of white blood cells’, Meas. J. Int. Meas. Confed., vol. 143, pp. 180–190, 2019, doi: 10.1016/j.measurement.2019.01.002.
H. Shayeste and B. M. Asl, ‘Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG’, Biomed. Signal Process. Control, vol. 79, no. P1, p. 104109, 2022, doi: 10.1016/j.bspc.2022.104109.
C. I. Ossai and N. Wickramasinghe, ‘GLCM and statistical features extraction technique with Extra-Tree Classifier in Macular Oedema risk diagnosis’, Biomed. Signal Process. Control, vol. 73, no. November 2021, p. 103471, 2022, doi: 10.1016/j.bspc.2021.103471.
Y. Park and J. M. Guldmann, ‘Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?’, Ecol. Indic., vol. 109, no. June 2019, p. 105802, 2020, doi: 10.1016/j.ecolind.2019.105802.
F. T. Kurniati, D. H. F. Manongga, E. Sediyono, S. Y. J. Prasetyo, and R. R. Huizen, ‘Object Classification Model Using Ensemble Learning with Gray- Level Co-Occurrence Matrix and Histogram Extraction’, J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 3, pp. 793–801, 2023, doi: 10.26555/jiteki.v9i3.26683.
M. E. Hossain, M. A. Kabir, L. Zheng, D. L. Swain, S. McGrath, and J. Medway, ‘A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions’, Artif. Intell. Agric., vol. 6, pp. 138–155, 2022, doi: 10.1016/j.aiia.2022.09.002.
A. M. Vommi and T. K. Battula, ‘A binary Bi-phase mutation-based hybrid Equilibrium Optimizer for feature selection in medical datasets classification’, Comput. Electr. Eng., vol. 105, no. January 2022, p. 108553, 2023, doi: 10.1016/j.compeleceng.2022.108553.
N. Gupta, D. Gupta, A. Khanna, P. P. Rebouças Filho, and V. H. C. de Albuquerque, ‘Evolutionary algorithms for automatic lung disease detection’, Meas. J. Int. Meas. Confed., vol. 140, pp. 590–608, 2019, doi: 10.1016/j.measurement.2019.02.042.
M. Kumar, S. Gupta, and N. Mohan, ‘A computational approach for printed document forensics using SURF and ORB features’, Soft Comput., vol. 24, no. 17, pp. 13197–13208, 2020, doi: 10.1007/s00500-020-04733-x.
M. N. A. Khan and R. M. Yunus, ‘A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries’, Nutrition, vol. 108, 2023, doi: 10.1016/j.nut.2022.111947.
Aditya Gumilar, Sri Suryani Prasetiyowati, and Yuliant Sibaroni, ‘Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)’, J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, pp. 481–490, 2022, doi: 10.29207/resti.v6i3.4142.
Copyright (c) 2024 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
