Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning

Aseel Sami Ali,Matheel E Abdulmunem

doi:10.36231/coedw/vol31no2.9

Abstract

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Convolutional Neural Network (CNN) has been chosen as a better option for the training process because it produces a high accuracy. The final accuracy has reached 91.18% in five different classes. The results are discussed in terms of the probability of accuracy for each class in the image classification in percentage. Cats class got 99.6 %, while houses class got 100 %.Other types of classes were with an average score of 90 % and above.

Highlights

Image classification is increasing and becoming a trend among technology developers especially with data growth in various parts of the industry such as e-commerce, automotive, healthcare and gaming
The result was checked by classifying five class types that showed high accuracy to the implementation of the image classification system using Convolutional Neural Network (CNN). This happened because of the amount of the data that was used, and that includes of 4 classes that were configured and downloaded from Kaggle and a class for SAR images that we have configured for their limitations, the preparing to house class start by capture the images, extract the desired objects to be expected and employed as posters ready for training, verification, and testing in the system
This research for image classification and overcome the difficulties of CNN's deep training resulting from dataset consist of limited SAR images by using a pre-trained model VGG16 for transfer learning which works as features extractor and it is an effective way to solve the problem of those who need to apply prediction from a limited dataset, using CNN as a classifier

Summary

Introduction

Image classification is increasing and becoming a trend among technology developers especially with data growth in various parts of the industry such as e-commerce, automotive, healthcare and gaming. Deep learning comes under the artificial intelligence umbrella, where it can behave or think like a human being. The device itself would usually be configured with hundreds of input data to make the training session more effective and swifter. It begins by giving a kind of' training' with all the input data (Patterson and Gibson, 2017). The deep learning system will be occupied with the classification of images When it comes to image recognition machine vision has its meaning. The combination of artificial intelligence software and computer vision technology will achieve the image classification outstanding result (Papernot, McDaniel, Jha, Fredrikson, Celik and Swami, 2016, pp.372387)

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