CNN based Image Classification Model
Annapurani. K1, Divya Ravilla2

1Dr.  Annapurani. K, Computer Science & Engineering,SRM Institute of Science and Technology, Chennai, Tamil Nadu, India. 

2Divya Ravilla,Computer Science & Engineering,SRM Institute of Science and Technology, Chennai, Tamil Nadu, India. 

Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1106-1114 | Volume-8 Issue-11S September 2019 | Retrieval Number: K122509811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1225.09811S19

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Abstract: Images are the fastest growing content, they contribute significantly to the amount of data generated on the internet every day. Image classification is a challenging problem that social media companies work on vigorously to enhance the user’s experience with the interface. The recent advances in the field of machine learning and computer vision enables personalized suggestions and automatic tagging of images. Convolutional neural network is a hot research topic these days in the field of machine learning. With the help of immensely dense labelled data available on the internet the networks can be trained to recognize the differentiating features among images under the same label. New neural network algorithms are developed frequently that outperform the state-of-art machine learning algorithms. Recent algorithms have managed to produce error rates as low as 3.1%. In this paper the architecture of important CNN algorithms that have gained attention are discussed, analyzed and compared and the concept of transfer learning is used to classify different breeds of dogs..

Keywords: Image Classification, Deep Learning, Machine Learning, Convolutional Neural Network .
Scope of the Article: Classification