Automatically Discovering Relevant Images From Web Pages

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dc.authorid0000-0002-3971-2676
dc.authorid0000-0002-9888-0151
dc.authorid0000-0003-4351-2244
dc.authorwosidOZHAN, Erkan/N-8743-2016
dc.contributor.authorUzun, Erdinç
dc.contributor.authorOzhan, Erkan
dc.contributor.authorAgun, Hayri Volkan
dc.contributor.authorYerlikaya, Tarık
dc.contributor.authorBuluş, Halil Nusret
dc.date.accessioned2022-05-11T14:03:00Z
dc.date.available2022-05-11T14:03:00Z
dc.date.issued2020
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractWeb pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.
dc.identifier.doi10.1109/ACCESS.2020.3039044
dc.identifier.endpage208921
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85183479365
dc.identifier.scopusqualityQ1
dc.identifier.startpage208910
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3039044
dc.identifier.urihttps://hdl.handle.net/20.500.11776/4569
dc.identifier.volume8
dc.identifier.wosWOS:000594426400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUzun, Erdinç
dc.institutionauthorOzhan, Erkan
dc.institutionauthorBuluş, Halil Nusret
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectWeb pages
dc.subjectFeature extraction
dc.subjectLayout
dc.subjectMachine learning
dc.subjectCrawlers
dc.subjectPredictive models
dc.subjectTask analysis
dc.subjectImage classification
dc.subjectimage retrieval
dc.subjectfeature extraction
dc.subjectweb crawlers
dc.subjectweb mining
dc.titleAutomatically Discovering Relevant Images From Web Pages
dc.typeArticle

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