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Which Saliency Detection Method is the Best to Estimate the Human Attention for Adjective Noun Concepts?

Marco Stricker, Syed Saqib Bukhari, Seyyed Saleh Mozaffari Chanijani, Mohammad Al-Naser, Damian Borth, Andreas Dengel

International Conference on Agents and Artificial Intelligence International Conference on Agents and Artificial Intelligence (ICAART), February 24-26 , scitepress , 2017
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.

Show BibTex:

@inproceedings {
       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.},
       number = {}, 
       month = {2}, 
       year = {2017}, 
       title = {Which Saliency Detection Method is the Best to Estimate the Human Attention for Adjective Noun Concepts?}, 
       journal = {}, 
       volume = {}, 
       pages = {}, 
       publisher = {scitepress}, 
       author = {Marco Stricker, Syed Saqib Bukhari, Seyyed Saleh Mozaffari Chanijani, Mohammad Al-Naser, Damian Borth, Andreas Dengel}, 
       keywords = {},
       url = {https://www.researchgate.net/publication/315865721_Which_Saliency_Detection_Method_is_the_Best_to_Estimate_the_Human_Attention_for_Adjective_Noun_Concepts, http://www.dfki.de/web/forschung/publikationen/renameFileForDownload?filename=ICAART_2017_96_CR.pdf&file_id=uploads_3265}
}