{"title":"Examining visual attention: A method for revealing users' interest for images on screen","authors":"Seyed Navid Haji Mirza, E. Izquierdo","doi":"10.1109/QoMEX.2011.6065706","DOIUrl":null,"url":null,"abstract":"This report tries to measure users' interest in images that appear on the screen by monitoring their attention via eye-tracking. Our Gaze Inference System analyzes the gaze-movement features to assign a user interest level (UIL) from 0 to 1 to every image that appears on the screen. Because the properties of the gaze features for every user are different from others, the framework is designed to be user adaptive. This framework is capable of building a new processing system for every new user that starts experiencing it. The generated UILs can be used in different scenarios that use the users' interest as an input. The developed framework produces promising and reliable results where 10% of the target images that the users were searching for received UILs over 0.8 with precision of 100%.","PeriodicalId":6441,"journal":{"name":"2011 Third International Workshop on Quality of Multimedia Experience","volume":"1 1","pages":"207-212"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Third International Workshop on Quality of Multimedia Experience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QoMEX.2011.6065706","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This report tries to measure users' interest in images that appear on the screen by monitoring their attention via eye-tracking. Our Gaze Inference System analyzes the gaze-movement features to assign a user interest level (UIL) from 0 to 1 to every image that appears on the screen. Because the properties of the gaze features for every user are different from others, the framework is designed to be user adaptive. This framework is capable of building a new processing system for every new user that starts experiencing it. The generated UILs can be used in different scenarios that use the users' interest as an input. The developed framework produces promising and reliable results where 10% of the target images that the users were searching for received UILs over 0.8 with precision of 100%.
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