Open AccessEmpirical Examination of Color Spaces in Deep Convolution Networks
Author(s) -
Urvi Oza,
Prof. P. Rajesh Kumar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b4038.079220
Subject(s) - rgb color model , artificial intelligence , color space , computer science , convolutional neural network , ycbcr , convolution (computer science) , deep learning , hsl and hsv , invariant (physics) , computer vision , color image , pattern recognition (psychology) , rgb color space , color histogram , image (mathematics) , artificial neural network , mathematics , image processing , mathematical physics , virus , virology , biology
In this paper we present an empirical examination of deep convolution neural network (DCNN) performance in different color spaces for the classical problem of image recognition/classification. Most such deep learning architectures or networks are applied on RGB color space image data set, so our objective is to study DCNNs performance in other color spaces. We describe the design of our novel experiment and present results on whether deep learning networks for image recognition task is invariant to color spaces or not. In this study, we have analyzed the performance of 3 popular DCNNs (VGGNet, ResNet, GoogleNet) by providing input images in 5 different color spaces(RGB, normalized RGB, YCbCr, HSV , CIE-Lab) and compared performance in terms of test accuracy, test loss, and validation loss. All these combination of networks and color spaces are investigated on two datasets- CIFAR 10 and LINNAEUS 5. Our experimental results show that CNNs are variant to color spaces as different color spaces have different performance results for image classification task.