Table 1.

Performance in predicting human eye fixations when viewing color images. Comparison of our SUN algorithm (Method 1 using DoG filters and Method 2 using linear ICA features) with previous algorithms. The KL divergence metric measures the divergence between the saliency distributions at human fixations and at randomly shuffled fixations (see text for details); higher values therefore denote better performance. The ROC metric measures the area under the ROC curve formed by attempting to classify points attended on the current image versus points attended in different images from the test set based on their saliency (Tatler et al., 2005).

Model	KL(SE)	ROC(SE)
Itti et al. (1998)	0.1130 (0.0011)	0.6146 (0.0008)
Bruce and Tsotsos (2006)	0.2029 (0.0017)	0.6727 (0.0008)
Gao and Vasconcelos (2007)	0.1535 (0.0016)	0.6395 (0.0007)
SUN: Method 1 (DoG filters)	0.1723 (0.0012)	0.6570 (0.0007)
SUN: Method 2 (ICA filters)	0.2097 (0.0016)	0.6682 (0.0008)