Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00138-011-0372-6
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dc.titleBottom-up saliency detection for attention determination
dc.contributor.authorGe, S.S.
dc.contributor.authorHe, H.
dc.contributor.authorZhang, Z.
dc.date.accessioned2014-06-17T02:40:35Z
dc.date.available2014-06-17T02:40:35Z
dc.date.issued2013-01
dc.identifier.citationGe, S.S., He, H., Zhang, Z. (2013-01). Bottom-up saliency detection for attention determination. Machine Vision and Applications 24 (1) : 103-116. ScholarBank@NUS Repository. https://doi.org/10.1007/s00138-011-0372-6
dc.identifier.issn09328092
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55222
dc.description.abstractIn this paper, the technique of saliency detection is proposed to model people's biological ability of attending to their interest. There are two phases in the scheme of intelligent saliency searching: saliency filtering and saliency refinement. In saliency filtering, non-salient regions of a scene image are filtered out by measuring information entropy and biological color sensitivity. The information entropy evaluates the level of knowledge and energy contained, and the color sensitivity measures biological stimulation of a presented scene. In saliency refinement, candidate salient regions obtained are cultivated for a good representation of saliency by extracting salient objects, similarly to people's manner of perception. The performance of the proposed technique is studied on noiseless and noisy natural scenes and evaluated with eye fixation data. The evaluation proved the effectiveness of the approach in discovering salient regions or objects from scene images. The performance of addressing transformation and illumination variance is also investigated. © 2011 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s00138-011-0372-6
dc.sourceScopus
dc.subjectColor sensitivity
dc.subjectGraph cuts
dc.subjectInformational entropy
dc.subjectSaliency
dc.subjectSocial robot
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/s00138-011-0372-6
dc.description.sourcetitleMachine Vision and Applications
dc.description.volume24
dc.description.issue1
dc.description.page103-116
dc.description.codenMVAPE
dc.identifier.isiut000313097100007
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