Optimal Cue Combination for Saliency Computation: A Comparison with Human Vision
The computer model of visual attention derives an interest or saliency map from an input image in a process that encompasses several data combination steps. While several combination strategies are possible, not all perform equally well. This paper compares main cue combination strategies by measuring the performance of the considered models with respect to human eye movements. Six main combination methods are compared in experiments involving the viewing of 40 images by 20 observers. Similarity is evaluated qualitatively by visual tests and quantitatively by use of a similarity score. The study provides insight into the map combination mechanisms and proposes in this respect an overall optimal strategy for a computer saliency model.
Unable to display preview. Download preview PDF.
- 5.Koch, C., Ullman, S.: Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)Google Scholar
- 6.Itti, L., Koch, C.: A comparison of feature combination strategies for saliency-based visual attention systems. In: SPIE Human Vision and Electronic Imaging IV (HVEI’99), vol. 3644, pp. 373–382 (1999)Google Scholar
- 7.Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. PAMI 28(5) (2006)Google Scholar
- 9.Ouerhani, N., Jost, T., Bur, A., Hugli, H.: Cue normalization schemes in saliency-based visual attention models. In: International Cognitive Vision Workshop, Graz, Austria (2006)Google Scholar
- 11.Ouerhani, N., von Wartburg, R., Hugli, H., Mueri, R.: Empirical validation of the saliency-based model of visual attention. Electronic Letters on Computer Vision and Image Analysis (ELCVIA) 3(1), 13–24 (2004)Google Scholar
- 12.Itti, L.: Quantitative modeling of perceptual salience at human eye position. Visual Cognition, in press (2005)Google Scholar