Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67525
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorHu, Y-
dc.creatorChen, Z-
dc.creatorChi, Z-
dc.creatorFu, H-
dc.date.accessioned2017-07-27T08:33:37Z-
dc.date.available2017-07-27T08:33:37Z-
dc.identifier.isbn978-1-4799-8697-2 (electronic)-
dc.identifier.isbn978-1-4799-8696-5 (USB)-
dc.identifier.urihttp://hdl.handle.net/10397/67525-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.subjectConvolutional neural networken_US
dc.subjectSaliency detectionen_US
dc.subjectSaliency mapen_US
dc.subjectDeep learningen_US
dc.titleLearning to detect saliency with deep structureen_US
dc.typeConference Paperen_US
dc.identifier.spage1770-
dc.identifier.epage1775-
dc.identifier.doi10.1109/SMC.2015.310-
dcterms.abstractDeep learning has shown great successes in solving various problems of computer vision. To the best of our knowledge, however, little existing work applies deep learning to saliency modeling. In this paper, a new saliency model based on convolutional neural network is proposed. The proposed model is able to produce a saliency map directly from an image's pixels. In the model, multi-level output values are adopted to simulate continuous values in a saliency map. Differing from most neural networks that use a relatively small number of output nodes, the output layer of our model has a large number of nodes. To make the training more efficient, an improved learning algorithm is adopted to train the model. Experimental results show that the proposed model succeeds in generating acceptable saliency maps after proper training.-
dcterms.bibliographicCitation2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, Hong Kong, China, 9-12 Oct 2015, p.1770-1775-
dcterms.issued2015-
dc.relation.conferenceIEEE International Conference on Systems, Man, and Cybernetics [SMC]-
dc.identifier.rosgroupid2015005325-
dc.description.ros2015-2016 > Academic research: refereed > Refereed conference paper-
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