Visual saliency model based on minimum description length
In this paper, a novel patch-wise visual saliency model based on Minimum Length Description (MDL) principle is presented. Visual saliency is measured as the unpredicted information of image patch through an order-adaptive predictor under MDL principle. Specifically, each image patch is estimated with a linear combination of several neighboring patches. The number and location of candidate patches are automatically tuned to local contexts based on MDL. Then the entropy of prediction residuals of center patch, which represents the surprise to the visual system, is used to measure the saliency. Furthermore, a structural redundancy operator is also involved to improve the saliency detection performance. Experimental results demonstrate that the predictor under MDL principle along with the structural redundancy operator can improve the accuracy of human fixations prediction. We show that the proposed model outperforms the mainstream algorithms in predicting human fixations.