salihah yousuf


The machine learning technology has received increased attention in recent years in several vision tasks such as image classification, image detection, and image recognition. In particular, recent advances of machine learning techniques bring encouragement to image classification with convolutional neural networks. CNN has been established as a powerful class of models for image recognition problems and even in some cases they outperform humans. The main purpose of the work presented in this paper is the rise and development of machine learning, deep learning, CNN and to give an overview on using machine learning for image classification. At the end the comparison of CNN with traditional method is discussed.


Convolutional Neural Networks, Deep Learning, Neural Networks

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Wei Wang, Gang Chen, Haibo Chen.Deep Learning at Scale and at Ease. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). 12 (4s), 69-94 (2016).

Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep Learning. Nature. 521 (1), 436-444 (2015).

Geoffrey E. Hinton, Simon Osindero, Yee-Whye Teh. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation. 18 (1), 1527–1554 (2006).

Li Deng and Dong Yu. Deep Learning Methods and Applications. Foundations and Trends in Signal Processing. 7 (3-4), 197-387 (2013).

Tom M. Mitchell. The Discipline of Machine Learning. CMU-ML. 06 (1), 108 (2006).

Geoffrey Hinton Where do features come from?. Canada: Department of Computer Science, University of Toronto. 1-33 (2013).

Rich Caruana and Alexandru Niculescu-Mizil. An Empirical Comparison of Supervised Learning Algorithms. International Con- ference on Machine Learning, Pittsburgh, PA, 2006. 23 (1), 1-8 (2006).

Seema Sharma, Jitendra Agrawal, Shikha Agarwal, Sanjeev Sharma. Machine Learning Techniques for Data Mining: A Survey. IEEE. 978-1-4799-1597 (2), 2-13 (2013).

Alex Krizhevsky and Georey E. Hinton. Using Very Deep Autoencoders for Content-Based Image Retrieval. University of Toronto - Department of Computer Science. 2 (18), 34-56 (2011).

Wei-Lun Chao. Machine Learning Tutorial. Taiwan: DISP Lab, Graduate Institute of Communication Engineering, National Taiwan University. 1-56 (2011).

Wei Wang. Big Data, Big Challenges. IEEE. 11 (3), 34-56 (2014).

Keiron O’Shea and Ryan Nash .An Introduction to Convolutional Neural Networks.arXiv:1511.08458v2 (2015).

Samer hifazi, Rishi kumar , and Chris rowen. Using convolutional neural networks for image recognition (2015).

Abdelkrim, A., & Loussaief, S. Machine learning framework for image classification. 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 58-61 (2016).

Sandeep chaplot, C.M Patnaik, N.R jagannanthan. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. 86-92 (2006).

Waseem Rawat and Zenghui Wang. Deep Convolutional Neural Networks for Image Classification; A Comprehensive Review. 29:9, 2352-2449 (2017).

Bo Zhao1,2 Jiashi Feng2 Xiao Wu1 Shuicheng Yan2. A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation. 119-135 (2017).

Ansam a.abdul hussien. Comparison of machine learning algorithms to classify web pages. Vol. 8, No. 11 (2017).

Marée R., Geurts P., Visimberga G., Piater J., Wehenkel L. A Comparison of Generic Machine Learning Algorithms for Image Classification. In: Coenen F., Preece A., Macintosh A. (eds) Research and Development in Intelligent Systems XX. Springer, London (2004).

Bhaskar, J., & Patel, A.P. Image Classification using Convolutional Neural Network (2016).

Yim J., Ju J., Jung H., Kim J. Image Classification Using Convolutional Neural Networks With Multi-stage Feature. In: Kim JH., Yang W., Jo J., Sincak P., Myung H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham (2015).

Weis, Martin & Rumpf, T & Gerhards, Roland & Plümer, Lutz. Comparison of different classification algorithms for weed detection from images based on shape parameters. ISSN 0947-7314 (2018).

Al-Saffar, A.A.M., Tao, H. and Talab, M.A.,October. Review of deep convolution neural network in image classification. In Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2017 International Conference on (pp. 26-31). IEEE (2017).

Bayot, R. and Gonçalves, T.A Survey on Object Classification using Convolutional Neural Networks(2015).

Lee, A.,.Comparing Deep Neural Networks and Traditional Vision Algorithms in Mobile Robotics. Swarthmore College(2015).



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