An edge detection with automatic scale selection approach to improve coherent visual attention model

Abstract

An automatic scale selection approach is developed to improve the coherent visual attention model (Le Meur, O., Le Callet, P., Barba, D., Thoreau, D., 2006. A coherent computational approach to model bottom-up visual attention. IEEE Trans. Pattern Anal. Machine Intell. 28 (5), 802–817). The new approach uses linear regression to combine the automatic scale selection attention model with the coherent visual attention model. It is biologically more plausible because two important properties (i.e. edge detection and scale selection) of human vision are taken into account. Its performance is evaluated using a large human fixation dataset. The t-test indicates that the improved model outperforms the coherent visual attention model highly significantly in both the non-weighting and weighting cases. The new model also outperforms seven other state-of-the-art saliency prediction models highly significantly (p<0.01). Thus it furnishes a more accurate model for human visual attention prediction.