Publication Type : Conference Paper
Publisher : Procedia Computer Science
Source : Procedia Computer Science, Elsevier B.V., Volume 143, p.954-961 (2018)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058333628&doi=10.1016%2fj.procs.2018.10.339&partnerID=40&md5=c084fd6d352cd054d32b508da8643122
Keywords : Classification (of information), Color, Convolution, Convolutional neural network, Deep learning, Feature vectors, Fully-connected layers, Gray-scale images, high performance computing, image classification, Information use, Learning algorithms, Neural networks, Program processors, Scene classification, Shape and textures, Singular value decomposition, Transfer learning
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2018
Abstract : Scene classification is considered as an imperative issue for computer vision and has got extensive consideration in the recent past. Due to recent developments in high performance computing units such as GPUs, popularly known deep learning algorithm namely, convolutional neural networks (CNNs), exploits huge datasets to give powerful models. The paper proposes the use of transfer learning technique, by which a pre-trained model known as Places-CNN is used to generate feature vectors for each scene image of the dataset. The scene-classification experiments are conducted on the Oliva Torralba (OT) scene dataset, which consists of eight outdoor scene categories. The features were extracted from the fully connected layer of the pre-trained Places CNN architecture. The deep features were extracted from the input color images and the grayscale images converted using two different techniques based on singular value decomposition (SVD). The results obtained from classification experiments show that, models trained on SVD-Decolorized and Modified-SVD decolorized images give comparable performance to the input color images. Unlike the color images, which use three planes (RGB) of information, the grayscale images use only one plane of information. The grayscale images were able to retain the required shape and texture information from the original RGB images and, thus sufficient to categorize the classes of scene images. © 2018 The Authors. Published by Elsevier B.V.
Cite this Research Publication : N. Damodaran, Sowmya, Govind, D., and Dr. Soman K. P., “Effect of decolorized images in scene classification using deep convolution features”, in Procedia Computer Science, 2018, vol. 143, pp. 954-961.