[Back]

J. Weibel, H. Li Tan, S. Lu:
"An Integrated Approach To Visual Attention Modelling Using Spatial-Temporal Saliency And Objectness";
Talk: IEEE Int. Conference on Image Processing (ICIP), Beijing, China; 09-17-2017 - 09-20-2017; in: "Ieee Icip 2017", (2017), 5 pages.

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 topdown
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.