Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-15561-1_3
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dc.titleAn eye fixation database for saliency detection in images
dc.contributor.authorRamanathan, S.
dc.contributor.authorKatti, H.
dc.contributor.authorSebe, N.
dc.contributor.authorKankanhalli, M.
dc.contributor.authorChua, T.-S.
dc.date.accessioned2013-07-04T08:26:06Z
dc.date.available2013-07-04T08:26:06Z
dc.date.issued2010
dc.identifier.citationRamanathan, S.,Katti, H.,Sebe, N.,Kankanhalli, M.,Chua, T.-S. (2010). An eye fixation database for saliency detection in images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6314 LNCS (PART 4) : 30-43. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-15561-1_3" target="_blank">https://doi.org/10.1007/978-3-642-15561-1_3</a>
dc.identifier.isbn364215560X
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41377
dc.description.abstractTo learn the preferential visual attention given by humans to specific image content, we present NUSEF- an eye fixation database compiled from a pool of 758 images and 75 subjects. Eye fixations are an excellent modality to learn semantics-driven human understanding of images, which is vastly different from feature-driven approaches employed by saliency computation algorithms. The database comprises fixation patterns acquired using an eye-tracker, as subjects free-viewed images corresponding to many semantic categories such as faces (human and mammal), nudes and actions (look, read and shoot). The consistent presence of fixation clusters around specific image regions confirms that visual attention is not subjective, but is directed towards salient objects and object-interactions. We then show how the fixation clusters can be exploited for enhancing image understanding, by using our eye fixation database in an active image segmentation application. Apart from proposing a mechanism to automatically determine characteristic fixation seeds for segmentation, we show that the use of fixation seeds generated from multiple fixation clusters on the salient object can lead to a 10% improvement in segmentation performance over the state-of-the-art. © 2010 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-15561-1_3
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-15561-1_3
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6314 LNCS
dc.description.issuePART 4
dc.description.page30-43
dc.identifier.isiutNOT_IN_WOS
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