Decorrelation and distinctiveness provide with human-like saliency

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5807 LNCS, Issue , 2009, Pages 343-354

  Garcia Diaz, Antón ^a   Fdez Vidal, Xosé R ^a   Pardo, Xosé M ^a   Dosil, Raquel ^a  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

Attention; Bottom up; Eye fixations; Saliency

Indexed keywords

DECORRELATIONS; HOTELLING'S T; LOCAL RESPONSE; LOW-LEVEL FEATURES; MACHINE VISION SYSTEMS; MULTISCALES; NEW MODEL; PLAUSIBLE MECHANISMS; PSYCHOPHYSICAL; STATE OF THE ART; VISUAL ATTENTION MODELING; VISUAL FUNCTIONS;

COMPUTATIONAL COMPLEXITY; COMPUTER VISION;

PRINCIPAL COMPONENT ANALYSIS;

EID: 70549106590     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04697-1_32     Document Type: Conference Paper

References (25)

1. 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)
2. 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)
3. 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)
4. Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489-1506 (2000)
5. 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)
6. Bruce, N., Tsotsos, J.K.: Saliency Based on Information Maximization. Advances in Neural Information Processing Systems 18, 155-162 (2006)
7. Bruce, N., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1-24 (2009)
8. Gao, D., Mahadevan, V., Vasconcelos, N.: On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision 8, 13 (2008)
9. 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)
10. Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. Advances in Neural Information Processing Systems 19, 545-552 (2007)
11. 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)
12. Olshausen, B.A., Field, D.J.: How Close Are We to Understanding V1? Neural Computation 17, 1665-1699 (2005)
13. 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)
14. 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)
15. 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)
16. Kovesi, P.: Invariant Measures of Image Features from Phase Information. Ph.D. Thesis, The University or Western Australia (1996)
17. 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)
18. Vinje, W.E., Gallant, J.L.: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273-1276 (2000)
19. Sharma, A., Paliwal, K.K.: Fast principal component analysis using fixed-point algorithm. Pattern Recognition Letters 28, 1151-1155 (2007)
20. Nothdurft, H.C.: The conspicuousness of orientation and motion contrast. Spatial Vision 7 (1993)
21. Treisman, A., Gormican, S.: Feature analysis in early vision: Evidence from search asymmetries. Psychological Review 95, 15-48 (1988)
22. 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)
23. 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)
24. 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)
25. Fawcett, T.: An introduction to ROC analysis. Pattern recognition letters 27, 861-874 (2006)