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

## TL;DR

This study analyzes eye-fixation data from videos to understand how visual attention correlates spatially, revealing significant neighborhood dependencies that inform models of video saliency.

## Contribution

It introduces an information-theoretic analysis of eye-fixation maps, highlighting spatial correlations without assuming specific map structures.

## Key findings

- Strong correlation between pixel saliency and neighbors
- Insights into the structure and dynamics of eye-fixation maps
- Implications for improving video saliency models

## Abstract

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.