Saliency prediction is a well studied problem in computer vision. Early saliency models were based on low-level hand-crafted feature derived from insights gained in neuroscience and psychophysics. In the wake of deep learning breakthrough, a new cohort of models were proposed based on neural network architectures, allowing significantly higher gaze prediction than previous shallow models, on all metrics. However, most models treat the saliency prediction as a problem, and accurate regression of high-dimensional data is known to be a hard problem. Furthermore, it is unclear that intermediate levels of saliency (ie, neither very high, nor very low) are meaningful: Something is either salient, or it is not. Drawing from those two observations, we reformulate the saliency prediction problem as a salient region problem. We demonstrate that the reformulation allows for faster convergence than the classical regression problem, while performance is comparable to state-of-the-art. We also visualise the general features learned by the model, which are showed to be consistent with insights from psychophysics.
The human visual system is foveated, that is, outside the central visual ﬁeld resolution and acuity drop rapidly.Nonetheless much of a visual scene is perceived after only a few saccadic eye movements, suggesting an effectivestrategy for selecting saccade targets. It has been known for some time that local image structure at saccade targetsinﬂuences the selection process. However, the question of what the most relevant visual features are is still under debate.Here we show that center-surround patterns emerge as the optimal solution for predicting saccade targets from their localimage structure. The resulting model, a one-layer feed-forward network, is surprisingly simple compared to previouslysuggested models which assume much more complex computations such as multi-scale processing and multiple featurechannels. Nevertheless, our model is equally predictive. Furthermore, our ﬁndings are consistent with neurophysiologicalhardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thoughtpreviously.Keywords: visual saliency, eye movements, receptive ﬁeld analysis, classiﬁcation images, kernel methods,support vector machines, natural scenesCitation: Kienzle, W., Franz, M. O., Scholkopf, B., & Wichmann, F. A. (2009). Center-surround patterns emerge as optimalpredictors for human saccade targets. Journal of Vision, 9(5):7, 1–15, http: journalofvision.org 9 5 7 , doi:10.1167 9.5.7.