An Analysis of Various Deep Learning Algorithms for Image Processing

EOI: 10.11242/viva-tech.01.02.13

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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.


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