An Image Statistics–Based Model for Fixation Prediction
Tóm tắt
The problem of predicting where people look at, or equivalently salient region detection, has been related to the statistics of several types of low-level image features. Among these features, contrast and edge information seem to have the highest correlation with the fixation locations. The contrast distribution of natural images can be adequately characterized using a two-parameter Weibull distribution. This distribution catches the structure of local contrast and edge frequency in a highly meaningful way. We exploit these observations and investigate whether the parameters of the Weibull distribution constitute a simple model for predicting where people fixate when viewing natural images. Using a set of images with associated eye movements, we assess the joint distribution of the Weibull parameters at fixated and non-fixated regions. Then, we build a simple classifier based on the log-likelihood ratio between these two joint distributions. Our results show that as few as two values per image region are already enough to achieve a performance comparable with the state-of-the-art in bottom-up saliency prediction.
Tài liệu tham khảo
Baddeley RJ, Tatler BW. High frequency edges (but not contrast) predict where we fixate: a Bayesian system identification analysis. Vis Res. 2006;46(18):2824–33.
Bonnans J, Lemaréchal C. Numerical optimization: theoretical and practical aspects. Springer, New York; 2006.
Brainard DH. The psychophysics toolbox. Spat Vis. 1997;10(4):433–6.
Bruce N, Tsotsos J. Saliency, attention, and visual search: an information theoretic approach. J Vis. 2009;9(3):1–24.
Cerf M, Harel J, Einhauser W, Koch C. Predicting human gaze using low-level saliency combined with face detection. Adv Neural Inf Process Syst. 2008;20:241–8.
Cornelissen FW, Peters EM, Palmer J. The eyelink toolbox: eye tracking with MATLAB and the psychophysics toolbox. Behav Res Methods Instrum Comput. 2002;34(4):613–7.
Einhauser W, Spain M, Perona P. Objects predict fixations better than early saliency. J Vis 2008;8(14):1–26.
Elazary L, Itti L. Interesting objects are visually salient. J Vis 2008;8(3).
Elder James H, Zucker Steven W. Local scale control for edge detection and Blur estimation. IEEE Trans Pattern Anal Mach Intell. 1998;20(7):699–716.
Gao D, Mahadevan V, Vasconcelos N. On the plausibility of the discriminant center-surround hypothesis for visual saliency. J Vis 2008;8(7):1–18.
Geisler WS. Visual perception and the statistical properties of natural scenes. Ann Rev Psychol. 2008;59:167–92.
Geusebroek JM, van den Boomgaard R, Smeulders AWM, Dev A. Color and scale: the spatial structure of color images. Eur Conf Comput Vis. 2000;1:331–41.
Geusebroek JM, van den Boomgaard R, Smeulders AWM, Geerts H. Color invariance. IEEE Trans Pattern Anal Mach Intell. 2001;23(12):1338–50.
Geusebroek JM, Smeulders AWM. A six-stimulus theory for stochastic texture. Int J Comput Vis. 2005;62(1):7–16.
Harel J, Koch C, Perona P. Graph-based visual saliency. Adv Neural Inf Process Syst. 2007;19:545–52.
Henderson JM. Human gaze control during real-world scene perception. Trends Cogn Sci. 2003;7(11):498–504.
Huang J, Mumford D. Statistics of natural images and models. IEEE Conf Comput Vis Pattern Recognit 1999;7(6).
Hubel DH, Wensveen J, Wick B. Eye, brain, and vision. Scientific American Library, New York; 1988.
Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell. 1998;20(11):1254–9.
Johnson NL, Kotz S, Balakrishnan N. Continuous univariate distributions. Vol. 1. Wiley, New York; 1995.
Judd T, Ehinger K, Durand F, Torralba A. Learning to Predict Where Humans Look. Int Conf Comput Vis. 2009.
Kadir T, Brady M. Saliency, scale and image description. Int J Comput Vis. 2001;45(2):83–105.
Kienzle W, Franz MO, Scholkopf B, Wichmann FA. Center-surround patterns emerge as optimal predictors for human saccade targets. J Vis. 2009;9(5):1–15.
Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.
Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell. 1989;11(7):674–93.
Mante V, Frazor RA, Bonin V, Geisler WS, Carandini M. Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci. 2005;8(12):1690–7.
Navalpakkam V, Itti L. Search goal tunes visual features optimally. Neuron 2007;53(4):605–17.
Parkhurst D, Law K, Niebur E. Modeling the role of salience in the allocation of overt visual attention. Vis Res. 2002;42(1):107–23.
Rajashekar U, van der Linde I, Bovik AC, Cormack LK. Foveated analysis of image features at fixations. Vis Res. 2007;47(25):3160–72.
Reinagel P, Zador A. Natural scene statistics at the centre of gaze. Netw Comput Neural Syst. 1999;10(4):341–50.
Renninger LW, Coughlan J, Verghese P, Malik J. An information maximization model of eye movements. Adv Neural Inf Process Syst. 2005;17:1121–8.
Ross SM. Introduction to probability and statistics for engineers and scientists. Elsevier, Amsterdam; 2009.
Rothkopf CA, Ballard DH, Hayhoe MM. Task and context determine where you look. J Vis. 2007;7(14):1–20.
Scholte HS, Ghebreab S, Waldorp L, Smeulders AWM, Lamme VAF. Brain responses strongly correlate with Weibull image statistics when processing natural images. J Vis. 2009;9(4):1–15.
Schumann F, Einhauser W, Vockeroth J, Bartl K, Schneider E, Konig P. Salient features in gaze-aligned recordings of human visual input during free exploration of natural environments. J Vis. 2008;8(14).
Seo HJ, Milanfar P. Nonparametric bottom-up saliency detection by self-resemblance. In: IEEE conference on computer vision and pattern recognition, 1st international workshop on visual scene understanding; 2009.
Simoncelli EP, Olshausen BA. Natural image statistics and neural representation. Ann Rev Neurosci. 2001;24(1):1193–216.
Tatler BW, Baddeley RJ, Gilchrist ID. Visual correlates of fixation selection: effects of scale and time. Vis Res. 2005;45(5):643–59.
Torralba A, Oliva A, Castelhano MS, Henderson JM. Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search. Psychol Rev. 2006;113(4):766–86.
Treisman AM, Gelade G. A feature-integration theory of attention. Cogn Psychol. 1980;12(1):97–136.
Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW. SUN: a Bayesian framework for saliency using natural statistics. J Vis. 2008;8(7):1–20.