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In this paper, we present an analysis of recorded eye-fixation data fromhuman subjects viewing video sequences. The purpose is to better understandvisual attention for videos. Utilizing the eye-fixation data provided in theCRCNS (Collaborative Research in Computational Neuroscience) dataset, thispaper focuses on the relation between the saliency of a pixel and that of itsdirect neighbors, without making any assumption about the structure of theeye-fixation maps. By employing some basic concepts from information theory,the analysis shows substantial correlation between the saliency of a pixel andthe saliency of its neighborhood. The analysis also provides insights into thestructure and dynamics of the eye-fixation maps, which can be very useful inunderstanding video saliency and its applications.

Understanding spatial correlation in eye-fixation maps for visual attention in videos