To manage the incremental change in the size of image data in various classification scenarios, requires methods based on appropriate visual content in a sensible measure of time. To make real-time decisions classification necessitates amended strategies, methodologies for processing, analysis, and classify on a large scale. This paper proposes an Image Classification method using feature fusion optimized by Kinetic Gas Molecule Optimization (KGMO). Multiple features such as Histogram of oriented Gradient (HoG), Compound Local Binary Pattern (CLBP), Color and Statistical features are considered. The extracted features are fused using KGMO algorithm. The proposed method utilizes KNN to find the distance between the feature vectors and is tested by comparing it with previous state-of-the-art techniques and it is found that it comparatively outperforms the state-of-the-art techniques.

[1] Badri N., Kboubi F., and Chaibi A., “Abusive and Hate Speech Classification in Arabic Text Using Pre-Trained Language Models and Data Augmentation,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 11, pp. 1-28, 2024. https://doi.org/10.1145/3592615

[2] Beane R., CIFAR-10 Image Classification, https://kaggle.com/competitions/mu-cifar10, Last Visited, 2025.

[3] Das R., Kumari K., De S., Manjhi P., and Thepade S., “Hybrid Descriptor Definition for Content Based Image Classification Using Fusion of Handcrafted Features to CNN Features,” International Journal of Information Technology, vol. 13, no. 4, pp. 1365-1374, 2021. https://doi.org/10.1007/s41870-021-00722-x

[4] Firuzi K., Vakilian M., Phung T., and Blackburn T., “Partial Discharges Pattern Recognition of Transformer Defect Model by LBP and HOG Features,” IEEE Transactions on Power Delivery, vol. 34, no. 2, pp. 542-550. https://doi.org/10.1109/TPWRD.2018.2872820

[5] Greeshma K. and Gripsy V., “Image Classification Using HOG and LBP Feature Descriptors with SVM and CNN,” International Journal of Engineering Research and Technology, vol. 8, no. 4, pp. 1-4, 2020. https://www.ijert.org/image-classification-using- hog-and-lbp-feature-descriptors-with-svm-and-cnn

[6] Hamza A., Javed A., Iqbal F., and et al., “Multimodal Religiously Hateful Social Media Memes Classification Based on Textual and Image Data,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 114, pp. 1-9, 2024. https://doi.org/10.1145/3623396

[7] Hlaing C. and Zaw S., “Tomato Plant Diseases Classification using Statistical Texture Feature and Color Feature,” in Proceedings of the IEEE/ACIS 17th International Conference on Computer and Information Science, Singapore, pp. 439-444, 2018. https://doi.org/10.1109/ICIS.2018.8466483

[8] Hwang S., Lee T., Kim H., Chung H., and et al., “Classification of Wood Knots Using Artificial Neural Networks with Texture and Local Feature- Based Image Descriptors,” Journal of Wood Research, vol. 76, no. 1, pp. 1-13, 2022. https://doi.org/10.1515/hf-2021-0051

[9] Iqbal F., Abbasi A., Javed A., and et al., “Data Augmentation-Based Novel Deep Learning Method for Deepfaked Images Detection,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 20, no. 339, pp. 1-15, 2024. https://doi.org/10.1145/359261

[10] Jaradat Y., Masoud M., Manasrah A., Alia M., Suwais K., and Almanasra S., “Exploring the 116 The International Arab Journal of Information Technology, Vol. 23, No. 1, January 2026 Intersection of Information Theory and Machine Learning,” The International Arab Journal of Information Technology, vol. 22, no. 5, pp. 845- 858, 2025. https://doi.org/10.34028/iajit/22/5/1

[11] Khan U., Javed A., and Ashraf R., “An Effective Hybrid Framework for Content Based Image Retrieval,” Multimedia Tools and Applications, vol. 80, no. 17, pp. 26911-26937, 2021. https://doi.org/10.1007/s11042-021-10530-x

[12] Kumar M. and Kumar M., “Performance Comparison of Various Feature Extraction Methods for Object Recognition on Caltech-101 Image Dataset,” in Proceedings of the Applications of Artificial Intelligence and Machine Learning, Singapore, pp. 289-303, 2021. https://doi.org/10.1007/978-981-16-3067-5_22

[13] Malegori C., Franzetti L., Guidetti R., and Casiraghi E., “GLCM, an Image Analysis Technique for Early Detection of Biofilm,” Journal of Food Engineering, vol. 185, pp. 48-55, 2016. https://doi.org/10.1016/j.jfoodeng.2016.04.001

[14] Mansourian L., Abdullah M., Abdullah L., Azman A., and Mustaffa M., “An Effective Fusion Model for Image Retrieval,” Journal of Multimedia Tools and Applications, vol. 77, no. 13, pp. 16131- 16154, 2018. https://doi.org/10.1007/s11042-017- 5192-x

[15] Matsui Y., Ito K., Aramaki Y., Yamasaki T., and Aizawa K., “Sketch-based Manga Retrieval Using Manga109 Dataset,” Journal of Multimedia Tools and Applications, vol. 76, no. 20, pp. 21811- 21838, 2017. https://doi.org/10.1007/s11042-016- 4020-z

[16] Moein S. and Logeswaran R., “KGMO: A Swarm Optimization Algorithm based on the Kinetic Energy of Gas Molecules,” Journal of Information Sciences, vol. 275, pp. 127-144, 2014. https://doi.org/10.1016/j.ins.2014.02.026

[17] Nayak V., Arunalatha J., Vasanthakumar G., and Venugopal K., “Design of Deep Convolution Features Extraction for Multimedia Information Retrieval,” International Journal of Intelligent Unmanned Systems, vol.11, no. 1, pp. 5-19, 2021. https://doi.org/10.1108/IJIUS-11-2021-0126

[18] Nilsback M. and Zisserman A., “Automated Flower Classification over a Large Number of Classes,” in Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image Processing, Bhubaneswar, pp. 722-729, 2008. https://doi.org/10.1109/ICVGIP.2008.47

[19] Rao S. and Mahantesh K., “Hybrid Model for Image Classification: Bridging the Gap with Global Feature Descriptor,” Journal of Data Acquisition and Processing, vol. 38, no. 3, pp. 2874-2883, 2023. DOI: 2023-03_2874.pdf

[20] Russakovsky O., Deng J., Su H., and et al., “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211-252, 2015. DOI https://doi.org/10.1007/s11263-015-0816-y

[21] Sliti O., Hamam H., and Amiri H., “CLBP for Scale and Orientation Adaptive Mean Shift Tracking,” Journal of King Saud University Computer and Information Sciences, vol. 30, no. 3, pp. 416-429, 2018. https://doi.org/10.1016/j.jksuci.2017.05.003

[22] Toennies K., An Introduction to Image Classification: From Designed Models to End-to- End Learning, Springer Singapore, 2024. https://doi.org/10.1007/978-981-99-7882-3

[23] Vatamanu O., Frandes M., Ionescu M., and Apostol S., “Content based Image Retrieval Using Local Binary Pattern, Intensity Histogram and Color Coherence Vector,” in Proceedings of the E-Health and Bioengineering Conference, Iasi, pp. 1-6, https://doi.org/10.1109/EHB.2013.6707396

[24] Wang P., Fan E., and Wang P., “Comparative Analysis of Image Classification Algorithms based on Traditional Machine Learning and Deep Learning,” Pattern Recognition Letters, vol. 141, pp. 61-67, 2021. https://doi.org/10.1016/j.patrec.2020.07.042

[25] Wang Y., Song B., Zhang P., Xin N., and Cao G., “A Fast Feature Fusion Algorithm in Image Classification for Cyber Physical Systems,” IEEE Access, vol. 5, pp. 9089-9098, 2017. https://doi.org/10.1109/ACCESS.2017.2705798

[26] Yang F., Ma Z., and Xie M., “Image Classification with Superpixels and Feature Fusion Method,” Journal of Electronic Science and Technology, vol. 19, no. 1, pp. 100096, 2021. https://doi.org/10.1016/j.jnlest.2021.100096

[27] Zhou J., Liu X., Xu T., Gan J., and Liu W., “A New Fusion Approach for Content Based Image Retrieval with Color Histogram and Local Directional Pattern,” International Journal of Machine Learning and Cybernetics, vol. 9, no. 4, pp. 677-689, 2018. https://doi.org/10.1007/s13042-016-0597-9