Publication Type : Conference Paper
Publisher : Springer Verlag
Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 900, p.743-752 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061092083&doi=10.1007%2f978-981-13-3600-3_71&partnerID=40&md5=8ff6be52f272bae3b3684c9126fa3efe
ISBN : 9789811335990
Keywords : Classification (of information), Classification accuracy, Computer graphics, Convolution, Convolutional neural network, Deep learning, Dimensionality reduction techniques, Graphics processing unit, image classification, Learning techniques, Neural networks, Principal component analysis, Program processors, rgb2gray, Scene classification, Singular value decomposition, Soft computing, Support vector machine (SVMs), Support vector machines, Textures, Transfer learning
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : Scene classification is considered as one of the challenging tasks of computer vision. Due to the availability of powerful graphics processing unit and millions of images, deep learning techniques such as convolutional neural networks (CNNs) have become popular in the image classification. This paper proposes the use of a pre-trained CNN model known as Places CNN, which was trained on scene-centric images. In this work, the pre-trained CNN is used as a feature extractor. The features are then used as input data for support vector machines (SVMs). The effect of grayscale images on the performance of pre-trained CNN-based scene classification system is analyzed by means of classification accuracy and equal error rate. The dataset used for this purpose is Oliva Torralba (OT) scene dataset, which consists of eight classes. The classification experiments are conducted using the feature vector from the ‘fc7’ layer of the CNN model for RGB, RGB2Gray, and SVD decolorized images. The classification experiment was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature vector. The results obtained from classification experiments show that RGB2Gray and SVD decolorized images were able to give results similar to that of RGB images. The grayscale images were able to retain the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of scene images. © Springer Nature Singapore Pte Ltd. 2019.
Cite this Research Publication : N. Damodaran, Sowmya, Govind, D., and Dr. Soman K. P., “Single-plane scene classification using deep convolution features”, in Advances in Intelligent Systems and Computing, 2019, vol. 900, pp. 743-752.