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

arXiv: https://arxiv.org/abs/1901.10957 · HTML: https://mlsanity.com/paper/1901.10957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes](https://mlsanity.com/paper/2408.08879/) (2024)

## Abstract (arXiv)

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.