Saliency Detection for Estimating the Task-driven Eye-movement

We propose a saliency model to estimate the task-driven eyemovement. Human eye movement patterns is affected by observer’s task and mental state[1]. However, the existing saliency model are detected from the low-level image features such as bright regions, edges, colors, etc., In this paper, the tasks(e.g., evaluation of a piano performance) are given to the observer who is watching the music videos. Unlike existing visual-based methods, we use score features and image features to detect a saliency. We show that our saliency model outperforms existing models that use eye movement patterns.