Understanding spatial correlation in eye-fixation maps for visual attention in videos
This provides insights into video saliency for applications in computational neuroscience and computer vision, but is incremental as it focuses on basic analysis of existing data.
The paper analyzed eye-fixation data from videos to understand visual attention, finding substantial correlation between pixel saliency and its neighborhood using information theory.
In this paper, we present an analysis of recorded eye-fixation data from human subjects viewing video sequences. The purpose is to better understand visual attention for videos. Utilizing the eye-fixation data provided in the CRCNS (Collaborative Research in Computational Neuroscience) dataset, this paper focuses on the relation between the saliency of a pixel and that of its direct neighbors, without making any assumption about the structure of the eye-fixation maps. By employing some basic concepts from information theory, the analysis shows substantial correlation between the saliency of a pixel and the saliency of its neighborhood. The analysis also provides insights into the structure and dynamics of the eye-fixation maps, which can be very useful in understanding video saliency and its applications.