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dc.contributor.authorGuo, Lihua
dc.contributor.authorLi, Fudi
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.editorLiew, AWC
dc.contributor.editorLovell, B
dc.contributor.editorFookes, C
dc.contributor.editorZhou, J
dc.contributor.editorGao, Y
dc.contributor.editorBlumenstein, M
dc.contributor.editorWang, Z
dc.date.accessioned2017-11-30T03:32:12Z
dc.date.available2017-11-30T03:32:12Z
dc.date.issued2016
dc.identifier.isbn9781509028962
dc.identifier.doi10.1109/DICTA.2016.7797049
dc.identifier.urihttp://hdl.handle.net/10072/124160
dc.description.abstractTraditional image aesthetic evaluation method usually involves the extraction of a set of relevant image aesthetic features and classification by a classifier trained on the set of features. The system's performance greatly depends on the effectiveness of the features. However, most of these features are carefully hand-crafted for specific datasets and assumed strong prior knowledge. Therefore, these features would not be optimal for general image aesthetic evaluation. The deep convolution neural network (DCNN) has the ability to automatically learn aesthetic features, and network structure of different complexity can learn aesthetic features at different scales and different point of views. Moreover, traditional image features, such as edge and saliency map, can be used as auxiliary information for the DCNN. Therefore, a Network-Paralleled and Data-Paralleled DCNN (NP-DP-DCNN) structure is proposed. The Network-Paralleled DCNN fuses networks of different complexity and the Data-Paralleled DCNN fuses original image data and derived feature maps to learn the aesthetic features from different scales and different point of views. Experimental results show that the proposed NP-DP-DCNN structure is able to achieve better classification performance than many existing methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeAustralia
dc.relation.ispartofconferencenameInternational Conference on Digital Image Computing - Techniques and Applications (DICTA)
dc.relation.ispartofconferencetitle2016 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
dc.relation.ispartofdatefrom2016-11-30
dc.relation.ispartofdateto2016-12-02
dc.relation.ispartoflocationGold Coast, AUSTRALIA
dc.relation.ispartofpagefrom76
dc.relation.ispartofpageto80
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleImage Aesthetic Evaluation using Parallel Deep Convolution Neural Network
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung


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