An Analysis of Various Deep Learning Algorithms for Image Processing
Geeta S. Lagad, Ankit J. Maurya, Kunal D. Mestry, Dnyaneshwar Bhabad, "An Analysis of Various Deep Learning Algorithms for Image Processing", VIVA-IJRI Volume 1, Issue 2, Article 13, pp. 1-6, 2019. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.
Various applications of image processing has given it a wider scope when it comes to data analysis. Various Machine Learning Algorithms provide a powerful environment for training modules effectively to identify various entities of images and segment the same accordingly. Rather one can observe that though the image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task, deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the image processing domain. It has way higher accuracy and computational power for classifying images further and segregating their various entities as individual components of the image working region. Major focus will be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
Image processing, data analysis, machine learning, support vector machine, random forest algorithms, deep learning, artificial neural networks, convolution neural networks, region convolution neural networks.
- S. Lai, L. Xu, K. Liu and J. Zhao, “Recurrent Convolutional Neural Networks for Text Classification”, Proceedings of the Twenty-Ninth AAAI Conference on AI 2015.
- P. Ongsulee, “Artificial Intelligence, Machine Learning and Deep Learning”, 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE)
- W. Yin, K. Kann, Mo Yu and H. Schütze, “Comparative study of CNN and RNN for Natural Language Processing”, Feb-17.
- Z.Shi, M. Shi and C. Li, “The prediction of character based on Recurrent Neural network language model”, 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).
- V. Tran, K. Nguyen and D. Bui, “A Vietnamese Language Model Based on Recurrent Neural Network”, 2016 Eighth International Conference on Knowledge and Systems Engineering.
- K. C. Arnold, K.Z. Gajos and A. T. Kalai, “On Suggesting Phrases vs. Predicting Words for Mobile Text Composition”; https://www.microsoft.com/enus/research/wpcontent/uploads /2016/12/ arnold16suggesting.pdf.
- J. Lee and F. Dernoncourt, “Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks”, Conference paper at NAACL 2016.
- M. Liang and X. Hu, “Recurrent Convolutional Neural Network for Object Recognition”, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- A. Hassan and A.Mahmood, “Deep Learning for Sentence Classification”, 2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT).
- J. Shin, Y. Kim and S. Yoon, “Contextual CNN: A Novel Architecture Capturing Unified Meaning for Sentence Classification”, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp).
- W. Yin and H. Schutze, “Multichannel Variable-Size Convolution for Sentence Classification”, 19th Conference on Computational Language Learning, c 2015 Association for Computational Linguistics.
- I. Sutskever, O. Vinyals and Q. V. Le, “Sequence to Sequence Learning with Neural Networks”, Dec-14.
- Y. Zhang, B. Wallace, “A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification”, arXiv: 1510.03820v4 [cs.CL], 2016.
- A. Salem, A. Almarimi, G Andrejková, “Text Dissimilarities Predictions Using Convolutional Neural Networks and Clustering” World Symposium on Digital Intelligence for Systems and Machines (DISA), 2018
- Y. Lin, J. Wang, “Research on text classification based on SVM-KNN” IEEE 5th International Conference on Software Engineering and Service Science, 2014
- A. Hassan, A. Mahmood, “Convolutional Recurrent Deep Learning Model for Sentence Classification”, IEEE Access, 2018