Open AccessImage based flower species classification using CNN
Author(s) -
Santosh Giri
Publication year - 2019
Publication title -
journal of innovations in engineering education
Language(s) - English
Resource type - Journals
eISSN - 2773-823X
pISSN - 2594-343X
DOI - 10.3126/jiee.v2i1.36670
Subject(s) - softmax function , artificial intelligence , classifier (uml) , computer science , pattern recognition (psychology) , feature extraction , transfer of learning , deep learning , artificial neural network , contextual image classification , convolutional neural network , machine learning , image (mathematics)
Deep learning is one of the essential parts of machine learning. Applications such as image classification, text recognition, object detection etc. used deep learning architectures. In this paper neural network model was designed for image classification. A NN classifier with one fully connected layer and one softmax layer was designed and feature extraction part of inception v3 model was reused to calculate the feature value of each images. And by using these feature values the NN classifier was trained. By adopting transfer learning mechanism NN classifier was trained with 17 classes of oxford 17 flower image dataset. The system provided final training accuracy of 99 %. After training, system was evaluated with testing dataset images. The mean testing accuracy was 86.4%.