Saliency in an image or video is a region of interest that stands out relative to its neighbors and consequently attracts more human attention. A key factor in designing an algorithm to measure the importance and distinctiveness (i.e. saliency) of different regions of a frame is to understand how different visual cues affect the human perceptual and visual system. To this end, we investigated bottom-up features including color, texture, and motion in 2D video sequences for both one-by-one and combined scenarios to provide a ranking system stating the most dominant circumstances for each feature individually and in combination with other features as well. In this work, we mostly considered the feature combination scenarios under conditions in which we had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgements and decision-making actions. Since computers do not typically have this ability, we tried to minimize this bias in the design of our experiment. First, we modelled our test data as 2D images and videos in a virtual environment to avoid any cognitive bias. Then, we performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed ranking system of salient visual attention stimuli was achieved using an eye-tracking procedure. This work provides a benchmark to specify the most salient stimulus with comprehensive information for both static and dynamic scenes. The main goal of this work is to create the ability of assigning a ranking of saliency for the entirety of an image/video frame rather than simply extracting a salient object/area which is widely performed in the state-of-the-art.

Additional Metadata
Keywords Bottom-up Features, Cognitive Bias, Dynamic Scenes, Saliency Detection, Visual Attention Model
Persistent URL dx.doi.org/10.1109/ISSPIT.2018.8642701
Conference 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018
Citation
Hosseinkhani, J. (Jila), & Joslin, C. (2019). Investigating into Saliency Priority of Bottom-up Attributes in 2D Videos Without Cognitive Bias. In 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018 (pp. 223–228). doi:10.1109/ISSPIT.2018.8642701