Categorized Image Classification Using CNN Features with ECOC Framework
Shameem Fatima1, M. Seshashayee2 

1Shameem Fatima, Department of CS, GITAM (Deemed to be University), Visakhapatnam, India.
2Dr. M. Seshashayee, Department of CS, GITAM (Deemed to be University), Visakhapatnam, India.

Manuscript received on 01 March 2019 | Revised Manuscript received on 07 March 2019 | Manuscript published on 30 July 2019 | PP: 145-150 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1937058119/19©BEIESP | DOI: 10.35940/ijrte.A1937.078219
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Abstract: Image Classification technique is used to classify images into categories. In this study, an application is presented to examine category based image classification by combining Support Vector Machine with error correcting output codes (ECOC) framework. The ResNet50 used as Network architecture, our image dataset include caltech101 images from 9 categories (classes) which builds our classification task a multiclass problem. ECOC is a commonly used framework to model multiclass classification problem. We present one-verses-all coding design of ECOC and apply to SVM classifier. A pre-trained CNN (convolution neural network) is used for extracting image feature and as a classifier Multiclass Support Vector Machine is used. The extracted features are then passed for classification via ECOC approach. The final classification result predicts the class labels. The application is implemented in Matlab using pre-trained CNN. The prediction accuracy of each category is evaluated and presented. The experimental result shows an accuracy of 97.6%. Further experiments are carried out on different dataset which showed that best accuracy is achieved using CNN with ECOC for multiclass problem.
Index Terms: Convolution Neural Network, ECOC, Image Classification, SVM, ResNet50.

Scope of the Article: Classification