Semantically-based human scanpath estimation with HMMs

Liu, H; Xu, D; Huang, Q; Li, W; Xu, M; Lin, S

Semantically-based human scanpath estimation with HMMs

Liu, H Xu, D

Huang, Q Li, W Xu, M

Lin, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3232 - 3239
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Liu, H	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Huang, Q	en_US
dc.contributor.author	Li, W	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Lin, S	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3232 - 3239	en_US
dc.identifier.isbn	9781479928392	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28361
dc.description.abstract	We present a method for estimating human scan paths, which are sequences of gaze shifts that follow visual attention over an image. In this work, scan paths are modeled based on three principal factors that influence human attention, namely low-level feature saliency, spatial position, and semantic content. Low-level feature saliency is formulated as transition probabilities between different image regions based on feature differences. The effect of spatial position on gaze shifts is modeled as a Levy flight with the shifts following a 2D Cauchy distribution. To account for semantic content, we propose to use a Hidden Markov Model (HMM) with a Bag-of-Visual-Words descriptor of image regions. An HMM is well-suited for this purpose in that 1) the hidden states, obtained by unsupervised learning, can represent latent semantic concepts, 2) the prior distribution of the hidden states describes visual attraction to the semantic concepts, and 3) the transition probabilities represent human gaze shift patterns. The proposed method is applied to task-driven viewing processes. Experiments and analysis performed on human eye gaze data verify the effectiveness of this method. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE International Conference on Computer Vision	en_US
dc.relation.isbasedon	10.1109/ICCV.2013.401	en_US
dc.title	Semantically-based human scanpath estimation with HMMs	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

We present a method for estimating human scan paths, which are sequences of gaze shifts that follow visual attention over an image. In this work, scan paths are modeled based on three principal factors that influence human attention, namely low-level feature saliency, spatial position, and semantic content. Low-level feature saliency is formulated as transition probabilities between different image regions based on feature differences. The effect of spatial position on gaze shifts is modeled as a Levy flight with the shifts following a 2D Cauchy distribution. To account for semantic content, we propose to use a Hidden Markov Model (HMM) with a Bag-of-Visual-Words descriptor of image regions. An HMM is well-suited for this purpose in that 1) the hidden states, obtained by unsupervised learning, can represent latent semantic concepts, 2) the prior distribution of the hidden states describes visual attraction to the semantic concepts, and 3) the transition probabilities represent human gaze shift patterns. The proposed method is applied to task-driven viewing processes. Experiments and analysis performed on human eye gaze data verify the effectiveness of this method. © 2013 IEEE.

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http://hdl.handle.net/10453/28361