Abstract
In this work, we show the capability of a new model of saliency, of reproducing remarkable psychophysical results. The model presents low computational complexity compared to other models of the state of the art. It is based in biologically plausible mechanisms: the decorrelation and the distinctiveness of local responses. Decorrelation of scales is obtained from principal component analysis of multiscale low level features. Distinctiveness is measured through the Hotelling’s T2 statistic. The model is conceived to be used in a machine vision system, in which attention would contribute to enhance performance together with other visual functions. Experiments demonstrate the consistency with a wide variety of psychophysical phenomena, that are referenced in the visual attention modeling literature, with results that outperform other state of the art models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tsotsos, J.K.: Computational foundations for attentive Processes. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 3–7. Elsevier Academia Press, Amsterdam (2005)
Nothdurft, H.C.: Salience of Feature Contrast. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 233–239. Elsevier Academia Press, Amsterdam (2005)
Zetzsche, C.: Natural Scene Statistics and Salient Visual Features. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 226–232. Elsevier Academia Press, Amsterdam (2005)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (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, 802–817 (2006)
Bruce, N., Tsotsos, J.K.: Saliency Based on Information Maximization. Advances in Neural Information Processing Systems 18, 155–162 (2006)
Bruce, N., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1–24 (2009)
Gao, D., Mahadevan, V., Vasconcelos, N.: On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision 8, 13 (2008)
Gao, D., Mahadevan, V., Vasconcelos, N.: The discriminant center-surround hypothesis for bottom-up saliency. In: Proceedings of the Neural Information Processing Systems (NIPS) Conference, Vancouver, Canada (2007)
Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. Advances in Neural Information Processing Systems 19, 545–552 (2007)
Garcia-Diaz, A., Fdez-Vidal, X.R., Pardo, X.M., Dosil, R.: Local energy variability as a generic measure of bottom-up salience. In: Yin, P.-Y. (ed.) Pattern Recognition Techniques, Technology and Applications, pp. 1–24. In-Teh, Vienna (2008)
Olshausen, B.A., Field, D.J.: How Close Are We to Understanding V1? Neural Computation 17, 1665–1699 (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)
Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nature Reviews. Neuroscience 5(6), 495–501 (2004)
Field, D.J.: Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells. Journal of the Optical Society of America A 4(12), 2379–2394 (1987)
Kovesi, P.: Invariant Measures of Image Features from Phase Information. Ph.D. Thesis, The University or Western Australia (1996)
Morrone, M.C., Burr, D.C.: Feature Detection in Human Vision: A Phase-Dependent Energy Model. Proceedings of the Royal Society of London B 235, 221–245 (1988)
Vinje, W.E., Gallant, J.L.: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273–1276 (2000)
Sharma, A., Paliwal, K.K.: Fast principal component analysis using fixed-point algorithm. Pattern Recognition Letters 28, 1151–1155 (2007)
Nothdurft, H.C.: The conspicuousness of orientation and motion contrast. Spatial Vision 7 (1993)
Treisman, A., Gormican, S.: Feature analysis in early vision: Evidence from search asymmetries. Psychological Review 95, 15–48 (1988)
Rosenholtz, R., Nagy, A.L., Bell, N.R.: The effect of background color on asymmetries in color search. Journal of Vision 4, 224–240 (2004)
Tatler, B.W., Baddeley, R.J., Gilchrist, I.D.: Visual correlates of fixation selection: Effects of scale and time. Vision Research 45, 643–659 (2005)
Cortes, C., Mohri, M.: Confidence intervals for the area under the ROC curve. In: Advances in Neural Information Processing Systems 17: Proceedings of The 2004 Conference, p. 305. MIT Press, Cambridge (2005)
Fawcett, T.: An introduction to ROC analysis. Pattern recognition letters 27, 861–874 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garcia-Diaz, A., Fdez-Vidal, X.R., Pardo, X.M., Dosil, R. (2009). Decorrelation and Distinctiveness Provide with Human-Like Saliency. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-04697-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04696-4
Online ISBN: 978-3-642-04697-1
eBook Packages: Computer ScienceComputer Science (R0)