Abstract
Much research has been concerned with the notion of bottom-up saliency in visual scenes, i.e. the contribution of low-level image features such as brightness, colour, contrast, and motion to the deployment of attention. Because the human visual system is obviously highly optimized for the real world, it is reasonable to draw inspiration from human behaviour in the design of machine vision algorithms that determine regions of relevance. In previous work, we were able to show that a very simple and generic grayscale video representation, namely the geometric invariants of the structure tensor, predicts eye movements when viewing dynamic natural scenes better than complex, state-of-the-art models. Here, we moderately increase the complexity of our model and compute the invariants for colour videos, i.e. on the multispectral structure tensor and for different colour spaces. Results show that colour slightly improves predictive power.
Keywords
- video saliency
- eye movements
- intrinsic dimension
- multispectral structure tensor
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bigün, J., Granlund, G.H., Wiklund, J.: Multidimensional orientation estimation with application to texture analysis and optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 775–790 (1991)
Dorr, M., Martinetz, T., Gegenfurtner, K., Barth, E.: Variability of eye movements when viewing dynamic natural scenes. Journal of Vision 10(10), 1–17 (2010)
Einhäuser, W., Spain, M., Perona, P.: Objects predict fixations better than early saliency. Journal of Vision 8(14), 11–26 (2008)
Elazary, L., Itti, L.: Interesting objects are visually salient. Journal of Vision 8(3), 1–15 (2008)
Itti, L., Baldi, P.: A principled approach to detecting surprising events in video. In: Proc. IEEE Conf on Computer Vision and Pattern Recognition, pp. 631–637 (2005)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Krieger, G., Rentschler, I., Hauske, G., Schill, K., Zetzsche, C.: Object and scene analysis by saccadic eye-movements: an investigation with higher-order statistics. Spatial Vision 13(2,3), 201–214 (2000)
Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 802–817 (2006)
Mota, C., Stuke, I., Barth, E.: The Intrinsic Dimension of Multispectral Images. In: MICCAI Workshop on Biophotonics Imaging for Diagnostics and Treatment, pp. 93–100 (2006)
Reinagel, P., Zador, A.M.: Natural scene statistics at the centre of gaze. Network: Computation in Neural Systems 10, 341–350 (1999)
Vig, E., Dorr, M., Barth, E.: Efficient visual coding and the predictability of eye movements on natural movies. Spatial Vision 22(5), 397–408 (2009)
Vig, E., Dorr, M., Martinetz, T., Barth, E.: A Learned Saliency Predictor for Dynamic Natural Scenes. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 52–61. Springer, Heidelberg (2010)
Zhang, L., Tong, M.H., Cottrell, G.W.: SUNDAy: Saliency Using Natural Statistics for Dynamic Analysis of Scenes. In: Proceedings of the 31st Annual Cognitive Science Conference, Amsterdam, Netherlands (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Dorr, M., Vig, E., Barth, E. (2012). Colour Saliency on Video. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-32615-8_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32614-1
Online ISBN: 978-3-642-32615-8
eBook Packages: Computer ScienceComputer Science (R0)