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Selective clustering for representative paintings selection

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DENG, Yingying, Fan TANG, Weiming DONG, Fuzhang WU, Oliver DEUSSEN, Changsheng XU, 2019. Selective clustering for representative paintings selection. In: Multimedia Tools and Applications. 78(14), pp. 19305-19323. ISSN 1380-7501. eISSN 1573-7721. Available under: doi: 10.1007/s11042-019-7271-7

@article{Deng2019-07Selec-45588, title={Selective clustering for representative paintings selection}, year={2019}, doi={10.1007/s11042-019-7271-7}, number={14}, volume={78}, issn={1380-7501}, journal={Multimedia Tools and Applications}, pages={19305--19323}, author={Deng, Yingying and Tang, Fan and Dong, Weiming and Wu, Fuzhang and Deussen, Oliver and Xu, Changsheng} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:creator>Deng, Yingying</dc:creator> <dc:date rdf:datatype="">2019-04-05T09:13:45Z</dc:date> <dc:contributor>Dong, Weiming</dc:contributor> <dspace:isPartOfCollection rdf:resource=""/> <dc:contributor>Wu, Fuzhang</dc:contributor> <dcterms:title>Selective clustering for representative paintings selection</dcterms:title> <dc:language>eng</dc:language> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Xu, Changsheng</dc:creator> <dcterms:hasPart rdf:resource=""/> <dc:creator>Tang, Fan</dc:creator> <dc:creator>Dong, Weiming</dc:creator> <dc:contributor>Tang, Fan</dc:contributor> <dc:creator>Deussen, Oliver</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Wu, Fuzhang</dc:creator> <dspace:hasBitstream rdf:resource=""/> <dc:contributor>Xu, Changsheng</dc:contributor> <dcterms:abstract xml:lang="eng">Selective classification (or rejection based classification) has been proved useful in many applications. In this paper we describe a selective clustering framework with reject option to carry out large-scale digital arts analysis. With the help of deep learning techniques, we extract content-style features from a pre-trained convolutional network for the paintings. By proposing a rejection mechanism under Bayesian framework, we focus on selecting style-oriented representative paintings of an artist, which is an interesting and challenging cultural heritage application. Two kinds of samples are rejected during the rejection based robust continuous clustering process. Representative paintings are selected during the selective clustering phase. Visual qualitative analysis on small painting set and large scale quantitative experiments on a subset of Wikiart show that the proposed rejection based selective clustering approach outperforms the standard clustering methods.</dcterms:abstract> <dcterms:available rdf:datatype="">2019-04-05T09:13:45Z</dcterms:available> <dc:contributor>Deussen, Oliver</dc:contributor> <dc:rights>terms-of-use</dc:rights> <bibo:uri rdf:resource=""/> <dc:contributor>Deng, Yingying</dc:contributor> <dcterms:rights rdf:resource=""/> <dcterms:issued>2019-07</dcterms:issued> </rdf:Description> </rdf:RDF>

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