An integrated approach to visual attention modelling using spatial-temporal saliency and objectness
Jean-Baptiste Weibel, Hui Li Tan, Shijian Lu · 2017
Visual attention modelling is an important research topic with a wide range of applications in visual tracking, perceptual quality assessment, re-targeting, video summarization, etc. In this paper, we propose a visual attention model that captures both bottom-up spatial-temporal saliency and top-down objectness. Leveraging on co-occurrence histograms, the proposed model captures a number of low-level cues including contrast, gradient, as well as, magnitude and gradient of optical flow. Additionally, the proposed model incorporates mid-level objectness cue which helps to boost the modelling performance greatly. The proposed model obtained superior AUC-ROCs when evaluated over the ASCMN dataset and the UCF Sports Action dataset.