Markov models for ocular fixation locations in the presence and absence of colour

TL;DR

This paper models human eye fixation points on images using Markov chains, revealing differences in gaze behavior with and without color, and employs clustering and Bayesian methods for model selection.

Contribution

It introduces a data-driven Markov model for eye fixations, incorporating clustering and Bayesian model selection, and compares gaze behavior with and without color information.

Findings

Markov models effectively capture fixation patterns.

Color removal alters eye movement behavior.

Bayesian criteria determine optimal number of salient regions.

Abstract

We propose to model the fixation locations of the human eye when observing a still image by a Markovian point process in R 2 . Our approach is data driven using k-means clustering of the fixation locations to identify distinct salient regions of the image, which in turn correspond to the states of our Markov chain. Bayes factors are computed as model selection criterion to determine the number of clusters. Furthermore, we demonstrate that the behaviour of the human eye differs from this model when colour information is removed from the given image.