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Authors: Marco Stricker 1 ; Syed Saqib Bukhari 1 ; Mohammad Al Naser 1 ; Saleh Mozafari 1 ; Damian Borth 1 and Andreas Dengel 2

Affiliations: 1 German Research Center for Artificial Intelligence (DFKI), Germany ; 2 German Research Center for Artificial Intelligence (DFKI) and Technical University of Kaiserslautern, Germany

ISBN: 978-989-758-220-2

Keyword(s): Saliency Detection, Human Gaze, Adjective Noun Pairs, Eye Tracking.

Related Ontology Subjects/Areas/Topics: AI and Creativity ; Artificial Intelligence ; Cognitive Systems ; Computational Intelligence ; Evolutionary Computing ; Soft Computing ; Symbolic Systems ; Vision and Perception

Abstract: This paper asks the question: how salient is human gaze for Adjective Noun Concepts (a.k.a Adjective Noun Pairs - ANPs)? In an existing work the authors presented the behavior of human gaze attention with respect to ANPs using eye-tracking setup, because such knowledge can help in developing a better sentiment classification system. However, in this work, only very few ANPs, out of thousands, were covered because of time consuming eye-tracking based data gathering mechanism. What if we need to gather the similar knowledge for a large number of ANPs? For example this could be required for designing a better ANP based sentiment classification system. In order to handle that objective automatically and without using an eye-tracking based setup, this work investigated if there are saliency detection methods capable of recreating the human gaze behavior for ANPs. For this purpose, we have examined ten different state-of-the-art saliency detection methods with respect to the ground-truths, which are human gaze pattern themselves over ANPs. We found very interesting and useful results that the Graph-Based Visual Saliency (GBVS) method can better estimate the human-gaze heatmaps over ANPs that are very close to human gaze pattern. (More)

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Paper citation in several formats:
Stricker, M.; Bukhari, S.; Al Naser, M.; Mozafari, S.; Borth, D. and Dengel, A. (2017). Which Saliency Detection Method is the Best to Estimate the Human Attention for Adjective Noun Concepts?.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 185-195. DOI: 10.5220/0006198901850195

@conference{icaart17,
author={Marco Stricker. and Syed Saqib Bukhari. and Mohammad Al Naser. and Saleh Mozafari. and Damian Borth. and Andreas Dengel.},
title={Which Saliency Detection Method is the Best to Estimate the Human Attention for Adjective Noun Concepts?},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={185-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006198901850195},
isbn={978-989-758-220-2},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Which Saliency Detection Method is the Best to Estimate the Human Attention for Adjective Noun Concepts?
SN - 978-989-758-220-2
AU - Stricker, M.
AU - Bukhari, S.
AU - Al Naser, M.
AU - Mozafari, S.
AU - Borth, D.
AU - Dengel, A.
PY - 2017
SP - 185
EP - 195
DO - 10.5220/0006198901850195

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