Abstract
Bottom up attention models suggest that human eye movements can be predicted by means of algorithms that calculate the difference between a region and its surround at different image scales where it is suggested that the more different a region is from its surround the more salient it is and hence the more it will attract fixations. Recent studies have however demonstrated that a dummy classifier which assigns more weight to the center region of the image out performs the best saliency algorithm calling into doubt the validity of the saliency algorithms and their associated bottom up attention models. In this paper, we performed an experiment using linear discrimination analysis to try to separate between the values obtained from the saliency algorithm for regions that have been fixated and others that haven’t. Our working hypothesis was that being able to separate the regions would constitute a proof as to the validity of the saliency model. Our results show that the saliency model performs well in predicting non-salient regions and highly salient regions but that it performs no better than a random classifier in the middle range of saliency.
Chapter PDF
Similar content being viewed by others
References
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, 1254–1259 (1998)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (2000)
Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2, 194–203 (2001)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)
Underwood, G., Humphreys, L., Cross, E.: Congruency, Saliency and Gist in the inspection of objects in natural scenes. In: Eye Movements: A Window on Mind and Brain, pp. 563–579. Elsevier (2007)
Walther, D.: Interactions of Visual Attention and Object Recognition: Computational Modeling, Algorithms, and Psychophysics. PhD thesis, California Institute of Technology, Pasadena, California (2006)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of Neural Information Processing Systems (NIPS) (2006)
Cerf, M., Harel, J., Einhauser, W., Koch, C.: Predicting human gaze using low-level saliency combined with face detection. In: Advances in Neural Information Processing Systems (NIPS), vol. 20, pp. 241–248 (2007)
Henderson, J.M., Brockmole, J.R., Castelhano, M.S., Mack, M.: Visual Saliency Does Not Account for Eye Movements during Visual Search in Real-World Scenes. In: Eye Movements: A Window on Mind and Brain, pp. 537–562. Elsevier (2007)
Rajashekar, U., van der Linde, I., Bovik, A.C., Cormack, L.K.: Gaffe: A gaze-attentive fixation finding engine. IEEE Transactions on Image Processing 17, 564–573 (2008)
Meur, O.L., Callet, P.L., 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)
Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 185–207 (2013)
Borji, A., Sihite, D.N., Itti, L.: Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study. IEEE Transactions on Image Processing 22, 55–69 (2013)
Parkhurst, D., Law, K., Niebur, E.: Modeling the role of salience in the allocation of overt visual attention. Vision Research 42, 107–123 (2002)
Oliva, A., Torralba, A., Castelhano, M.S., Henderson, J.M.: Top-down control of visual attention in object detection. In: Proceedings of the 2003 International Conference on Image Processing, ICIP 2003, vol. 1, pp. 253–256 (2003)
Henderson, J.M.: Human gaze control during real-world scene perception. Trends in Cognitive Sciences 7, 498–504 (2003)
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)
Tatler, B.W.: The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. Journal of Vision 7, 1–17 (2007)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: International Conference on Computer Vision (ICCV) (2009)
Rosenholtz, R.: A simple saliency model predicts a number of motion popout phenomena. Vision Research 39, 3157–3163 (1999)
Cerf, M., Frady, E.P., Koch, C.: Faces and text attract gaze independent of the task: Experimental data and computer model. Journal of Vision 9, 1–15 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alsam, A., Sharma, P. (2013). Validating the Visual Saliency Model. In: Kämäräinen, JK., Koskela, M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38886-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-38886-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38885-9
Online ISBN: 978-3-642-38886-6
eBook Packages: Computer ScienceComputer Science (R0)