Automatic recognition of human emotions induced by visual contents of digital images based on color histogram

Mohseni, S, Wu, H and Thom, J 2017, 'Automatic recognition of human emotions induced by visual contents of digital images based on color histogram', in Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017), Sydney, Australia, 29 November - 1 December 2017, pp. 770-777.


Document type: Conference Paper
Collection: Conference Papers

Title Automatic recognition of human emotions induced by visual contents of digital images based on color histogram
Author(s) Mohseni, S
Wu, H
Thom, J
Year 2017
Conference name DICTA 2017
Conference location Sydney, Australia
Conference dates 29 November - 1 December 2017
Proceedings title Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017)
Publisher IEEE
Place of publication United States
Start page 770
End page 777
Total pages 8
Abstract Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.
Subjects Pattern Recognition and Data Mining
Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) color histogram
HSV color space
RGB color space
emotion recognition
optimum bin
number of bins in color histogram
emotions induced by visual contents of digital images
DOI - identifier 10.1109/DICTA.2017.8227410
Copyright notice © 2017 IEEE
ISBN 9781538628393
Versions
Version Filter Type
Altmetric details:
Access Statistics: 20 Abstract Views  -  Detailed Statistics
Created: Thu, 06 Dec 2018, 10:55:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us