Show simple item record

dc.contributor.authorGhosal, Koustav
dc.contributor.authorPrasad, Mukta
dc.contributor.authorSmolic, Aljosa
dc.date.accessioned2019-11-08T16:27:23Z
dc.date.available2019-11-08T16:27:23Z
dc.date.issued2018en
dc.date.submitted2018en
dc.identifier.citationGhosal, K., Prasad, M. & Smolic, A. 'A Geometry-Sensitive Approach for Photographic Style Classification', Irish Machine Vision and Image Processing Conference, Belfast, 2018en
dc.identifier.otherN
dc.identifier.urihttp://hdl.handle.net/2262/90363
dc.descriptionPUBLISHEDen
dc.description.abstractPhotographs are characterized by different compositional attributes like the Rule of Thirds, depth of field, vanishing-lines etc. The presence or absence of one or more of these attributes contributes to the overall artistic value of an image. In this work, we analyze the ability of deep learning based methods to learn such photographic style attributes. We observe that although a standard CNN learns the texture and appearance based features reasonably well, its understanding of global and geometric features is limited by two factors. First, the data-augmentation strategies (cropping, warping, etc.) distort the composition of a photograph and affect the performance. Secondly, the CNN features, in principle, are translationinvariant and appearance-dependent. But some geometric properties important for aesthetics, e.g. the Rule of Thirds (RoT), are position-dependent and appearance-invariant. Therefore, we propose a novel input representation which is geometry-sensitive, position-cognizant and appearance-invariant. We further introduce a two-column CNN architecture that performs better than the state-of-the-art (SoA) in photographic style classification. From our results, we observe that the proposed network learns both the geometric and appearance-based attributes better than the SoA.en
dc.description.sponsorshipSFIen
dc.language.isoenen
dc.rightsYen
dc.subjectDeep learning
dc.subjectConvolutional neural networks
dc.subjectComputational aesthetics
dc.titleA Geometry-Sensitive Approach for Photographic Style Classificationen
dc.title.alternativeIrish Machine Vision and Image Processing Conference 2018 (IMVIP), 2018.en
dc.typeConference Paperen
dc.contributor.sponsorScience Foundation Ireland (SFI)en
dc.type.supercollectionscholarly_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/smolica
dc.identifier.rssinternalid199007
dc.rights.ecaccessrightsopenAccess
dc.contributor.sponsorGrantNumber15/RP/2776en
dc.subject.TCDThemeCreative Technologiesen
dc.subject.TCDTagMultimedia & Creativityen
dc.status.accessibleNen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record