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dc.contributor.authorLuo, Xuejiaoen_US
dc.contributor.authorScandolo, Leonardoen_US
dc.contributor.authorEisemann, Elmaren_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:01:24Z
dc.date.available2021-06-12T11:01:24Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14292
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14292
dc.description.abstractTexture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre-trained convolutional neural network to extract high-level semantic image features, which are then mapped to a 2-dimensional location using an adaptation of t-SNE, a dimensionality-reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t-SNE, scalable clustering, and multi-resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman centerd computing
dc.subjectVisualization
dc.subjectVisualization systems and tools
dc.subjectVisualization toolkits
dc.titleTexture Browser: Feature-based Texture Explorationen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersMultivariate Data and Dimension Reduction
dc.description.volume40
dc.description.number3
dc.identifier.doi10.1111/cgf.14292
dc.identifier.pages99-109


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  • 40-Issue 3
    EuroVis 2021 - Conference Proceedings

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