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
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.
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