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Markov Models for Ocular Fixation Locations in the Presence and Absence of Colour

Markov models for ocular fixation locations in the presence and absence of colour
Authors: Kashlak, AB; Devane, E; Dietert, H; Jackson, H;

Markov Models for Ocular Fixation Locations in the Presence and Absence of Colour

Abstract

SummaryIn response to the 2015 Royal Statistical Society's statistical analytics challenge, we propose to model the fixation locations of the human eye when observing a still image by a Markov point process in R2. 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 the 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.

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United Kingdom
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Keywords

FOS: Computer and information sciences, 60J20 (primary), 62P10 (Secondary), Markov point process, Machine Learning (stat.ML), Applications of statistics, image saliency, Statistics - Applications, Statistics - Machine Learning, ocular fixation, Applications (stat.AP), Bayesian model selection, finite mixture model, cluster analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
hybrid