International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

Feature Extraction for Image Analysis And Detection Using Machine Learning Techniques


Affiliations
1 Faculty of Engineering and Information Technology, Arab American University, Jenin, Palestine., Palestinian Territory, Occupied
 

Feature extraction is the most vital step in image classification to produce high-quality and good content images for further analysis, image detection, segmentation, and object recognition. Using machine learning algorithms, profound learning like convolutional neural network CNN became necessary to train, classify, and recognize images and objects like humans. Combined feature extraction and machine learning classification to locate and identify objects on images can then be an input of automatic recognition systems ATR such as surveillance systems CCTV, to enhance these systems and reduce time and effort for object detection and recognition in images based on digital image processing techniques especially image segmentation that differentiate from computer vision approach. This article will use machine learning and deep learning algorithms to facilitate and achieve the study's objectives.

Keywords

Automatic Recognition Systems, Convolution Neural Network, Digital Image Processing, Feature Extraction, Image Segmentation.
User
Notifications
Font Size

  • Aparna Akula, Arshdeep Singh, Ripul Ghosh, Satish Kumar, and Hk Sardana, Target Recognition in Infrared Imagery Using Convolutional Neural Network, 2017.

  • Eren Golge. How does feature extraction work on images? URLhttps://www.quora.com/profile /ErenGolge/Machine-Learning/How-does-featureextraction-work-on-images.

  • F. Suard, A. Rakotomamonjy, and A. Bensrhair. Pedestrian detection using infrared images and histograms of oriented gradients. In IEEE Conference on Intelligent Vehicles, 2006.

  • K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. At British Machine Vision Conference, 2014.

  • MathWorks. Support vector machines for binary classification.URL https://se.mathworks.com/help/stats/ support-vectormachines-for-binary-classification.html.

  • Xia, J.; Ghamisi, P.; Yokoya, N.; Iwasaki, A. Random forest ensembles and extended multiextinction profiles for hyperspectral image classification, 2017.

  • Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. Trans, 2004.

  • David G. Lowe, Distinctive image features from scale-invariant key points," Int. J. Computer Vision, 2004.

  • Burhan Duman, Ahmet Ali Süzen, A Study on Deep Learning Based Classification of Flower Images, International Journal of Advanced Networking and Applications (IJANA), 2022.

  • He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017.MaskR-CNN,12pp. http://arxiv.org/pdf/1703.06870v3.

  • M Sharif, M Raza, S Mohsin, JH Shah, Microscopic Feature Extraction Method, International Journal of Advanced Networking and Application (IJANA), 2013.

  • Convolutional neural networks (lenet). URL http://deeplearning.net/ tutorial/lenet.html, 2020.

  • Ren, S., K. He, R. Girshick, and J. Sun, Faster rCNN: Towards real-time object detection with region proposal networks. In C. Cortes, N. D. Lawrence, D. D. Lee, and R. Garnett (Eds.), Advances in Neural Information Processing Systems , 2015.

  • Girshick R. Fast r-CNN. In: Proceedings of the IEEE international conference on computer vision; 2015.

  • Merentitis, A.; Debes, C.; Heremans, R. Ensemble learning in hyperspectral image classification: Toward selecting a favorable bias-variance tradeoff. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 2014.

  • Ling C., Bolun C., and Yixin C., Image Feature Selection Based on Ant Colony Optimization, 2011.

  • Uddin, M.P.; Mamun, MA; Hossain, M.A. PCAbased feature reduction for hyperspectral remote sensing image classification. IETE Tech. Rev. 2021

  • A. Riddhi, Vyas D., and Shah S. Comparison of PCA and LDA techniques for face recognition featurebased extraction with accuracy enhancement. IRJET, 2017.

  • R. Hendaoui, M. Abdellaoui, and A. Douik, "Synthesis of Spatio-temporal interest point detectors: Harris 3D, MoSIFT and SURF-MHI," in Proc. 1st Int. Conf. Adv. Technol. Signal Image Process, 2014.

  • Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In Proceedings of the Fourth ACM International Conference on Multimedia, 1996.

  • MathWorks. Discrete cosine transform, URL https://se.mathworks.com/help/images/discretecosine-transform.html, 2020.

  • Michael A. Nielsen. Neural Networks and Deep Learning. Determination Press, 2015.

  • Antoine d' Acremont, Ronan Fablet, Alexandre Baussard, and Guillaume Quin, Cnn-based target recognition and identification for infrared imaging in defense systems," Sensors, vol. 19, 2019.

  • Dominik Müller, Iñaki Soto-Rey and Frank Kramer, An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks, IT-Infrastructure for Translational Medical Research, 2022.

  • Lloyd, Stuart, least squares quantization in PCM, In IEEE transactions on information theory, 1982.

  • T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns IEEE Trans. Pattern Anal. Mach. Intell., 24 (7), 2002.

  • Huang, d., Shan, C., Ardabilian, M., Chen, L.: Local binary patterns and its application to facial image analysis: A survey. IEEE Transactions on Systems, Man, and Cybernetics, 2011.

  • Hanae Moussaoui, Mohamed Benslimane and Nabil El Akkad, Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN, Springer Singapore, 2020.

  • K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2016.

  • K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

  • J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. FeiFei, Imagenet: A large-scale hierarchical image database, in Computer Vision and Pattern Recognition, IEEE Conference on, 2009.

  • T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll'ar, C. L. Zitnick, Microsoft coco: Common objects in context, in European conference on computer vision, Springer, 2014.


Abstract Views: 134

PDF Views: 0




  • Feature Extraction for Image Analysis And Detection Using Machine Learning Techniques

Abstract Views: 134  |  PDF Views: 0

Authors

Adel Hassan
Faculty of Engineering and Information Technology, Arab American University, Jenin, Palestine., Palestinian Territory, Occupied
Muath Sabha
Faculty of Engineering and Information Technology, Arab American University, Jenin, Palestine., Palestinian Territory, Occupied

Abstract


Feature extraction is the most vital step in image classification to produce high-quality and good content images for further analysis, image detection, segmentation, and object recognition. Using machine learning algorithms, profound learning like convolutional neural network CNN became necessary to train, classify, and recognize images and objects like humans. Combined feature extraction and machine learning classification to locate and identify objects on images can then be an input of automatic recognition systems ATR such as surveillance systems CCTV, to enhance these systems and reduce time and effort for object detection and recognition in images based on digital image processing techniques especially image segmentation that differentiate from computer vision approach. This article will use machine learning and deep learning algorithms to facilitate and achieve the study's objectives.

Keywords


Automatic Recognition Systems, Convolution Neural Network, Digital Image Processing, Feature Extraction, Image Segmentation.

References