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Scene classification using transfer learning

Publication Type : Book Chapter

Publisher : Springer Verlag

Source : Studies in Computational Intelligence, Springer Verlag, Volume 804, p.363-399, Springer, Cham (2019)

Url :

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Verified : No

Year : 2019

Abstract : Categorization of scene images is considered as a challenging prospect due to the fact that different classes of scene images often share similar image statistics. This chapter presents a transfer learning based approach for scene classification. A pre-trained Convolutional Neural Network (CNN) is used as a feature extractor for the images. The pre-trained network along with classifiers such as Support Vector Machines (SVM) or Multi Layer Perceptron (MLP) are used to classify the images. Also, the effect of single plane images such as, RGB2Gray, SVD Decolorized and Modified SVD decolorized images are analysed based on classification accuracy, class-wise precision, recall, F1-score and equal error rate (EER). The classification experiment for SVM was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature vector. By comparing the results of models trained on RGB images with those grayscale images, the difference in the results is very small. These grayscale images were capable of retaining the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of the given scene images. © Springer Nature Switzerland AG 2019.

Cite this Research Publication : N. Damodaran, Sowmya V., Govind, D., and Dr. Soman K. P., “Scene Classification using transfer Learning”, in Studies in Computational Intelligence, vol. 804, Springer Verlag, 2019, pp. 363-399, Springer, Cham.

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