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Image Classification using Deep Learning

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Image Classification using Deep Learning

Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V. Gurme

Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V. Gurme "Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4, June 2020, pp.1648-1650, URL: https://www.ijtsrd.com/papers/ijtsrd31653.pdf

Most of the existing image recognitions systems are based on physical parameters of the images whereas image processing methodologies relies on extraction of color, shape and edge features. Thus Transfer Learning is an efficient approach of solving classification problem with little amount of data. There are many deep learning algorithms but most tested one is AlexNet. It is well-known Convolution Neural Network (AlexNet CNN) for recognition of images using deep learning. So for recognition and detection of the image we have proposed Deep Learning approach in this project which can analyse thousands of images which may take a lot for a human to do. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1000 images and can classify images into categories which we have defined. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. Thus when a image is passed to the system it will apply AlexNet and return the results with a image category in which the image lies with high accuracy. Thus our project tends to reduce time and cost of image recognition systems using deep learning.

Image Recognition, CNN, Transfer Learning, Median Filter, Deep Learning and AlexNet

Volume-4 | Issue-4, June 2020
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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