An Analysis on Multi – Image Classification Techniques
Thikshaya M1, Vishal C2

1Ms. Thikshaya M, Department of Computer Applications, RV College of Engineering, Bangalore, India.
2Mr. Vishal C,  Assistant Professor, Department of Computer Applications, RV College of Engineering, Bangalore, India.
Manuscript received on June 14, 2020. | Revised Manuscript received on June 25, 2020. | Manuscript published on July 10, 2020. | PP: 69-73 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I6887079920 | DOI: 10.35940/ijitee.I6887.079920
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Abstract: Image classification is a process where images are classified based on its visual content. It came into existence to reduce the gap between computer vision and human vision. To classify the images, humans involve lot of efforts and it is time consuming, in order to overcome this, technique such as convolutional neural network and random forest is being used. Convolutional neural network is a class of deep neural network and it is most commonly used for analyzing the images. Random forest is classification algorithms which consist of many independent decision trees. Auto encoding technique is being used to denoise the image. Image inpainting technique is adopted to come up with a complete image which contains missing parts. Image inpainting technique is a process to overcome overfitting. 
Keywords: Convolutional Neural Network, Random Forest, Deep Learning, Image Augmentation, Image In painting, Auto encoding, Machine Learning.
Scope of the Article: Classification