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== Reference ==
 
== Reference ==
Babak Saleh, Kanako Abe and Ahmed Elgammal: [[Knowledge Discovery of Artistic Influences: A Metric Learning Approach]]. In: [[Computational Creativity 2014 ICCC 2014]].  
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Babak Saleh, Kanako Abe and Ahmed Elgammal: [[Knowledge Discovery of Artistic Influences: A Metric Learning Approach]]. In: [[Computational Creativity 2014 ICCC 2014]], 163-172.  
  
 
== DOI ==
 
== DOI ==
Zeile 21: Zeile 21:
  
 
== Bibtex ==  
 
== Bibtex ==  
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@inproceedings{
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author = {Babak Saleh, Kanako Abe and Ahmed Elgammal},
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title = {Knowledge Discovery of Artistic Influences: A Metric Learning Approach},
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booktitle = {Proceedings of the Fifth International Conference on Computational Creativity},
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series = {ICCC2014},
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year = {2014},
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month = {Jun},
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location = {Ljubljana, Slovenia},
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pages = {163-172},
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url = {http://computationalcreativity.net/iccc2014/wp-content/uploads/2014/06//9.3_Saleh.pdf, http://de.evo-art.org/index.php?title=Knowledge_Discovery_of_Artistic_Influences:_A_Metric_Learning_Approach },
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publisher = {International Association for Computational Creativity},
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keywords = {computational, creativity},
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}
  
 
== Used References ==
 
== Used References ==

Aktuelle Version vom 12. November 2015, 13:10 Uhr

Reference

Babak Saleh, Kanako Abe and Ahmed Elgammal: Knowledge Discovery of Artistic Influences: A Metric Learning Approach. In: Computational Creativity 2014 ICCC 2014, 163-172.

DOI

Abstract

We approach the challenging problem of discovering in- fluences between painters based on their fine-art paint- ings. In this work, we focus on comparing paintings of two painters in terms of visual similarity. This compari- son is fully automatic and based on computer vision ap- proaches and machine learning. We investigated differ- ent visual features and similarity measurements based on two different metric learning algorithm to find the most appropriate ones that follow artistic motifs. We evaluated our approach by comparing its result with ground truth annotation for a large collection of fine-art paintings.

Extended Abstract

Bibtex

@inproceedings{
author = {Babak Saleh, Kanako Abe and Ahmed Elgammal},
title = {Knowledge Discovery of Artistic Influences: A Metric Learning Approach},
booktitle = {Proceedings of the Fifth International Conference on Computational Creativity},
series = {ICCC2014},
year = {2014},
month = {Jun},
location = {Ljubljana, Slovenia},
pages = {163-172},
url = {http://computationalcreativity.net/iccc2014/wp-content/uploads/2014/06//9.3_Saleh.pdf, http://de.evo-art.org/index.php?title=Knowledge_Discovery_of_Artistic_Influences:_A_Metric_Learning_Approach },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},
}

Used References

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Cabral, R. S.; Costeira, J. P.; De la Torre, F.; Bernardino, A.; and Carneiro, G. 2011. Time and order estimation of paintings based on visual features and expert priors. In SPIE Electronic Imaging, Computer Vision and Image Analysis of Art II.

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Dubuisson, M.-P., and Jain, A. K. 1994. A modified hausdorff distance for object matching. In Pattern Recognition.

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Sablatnig, R.; Kammerer, P.; and Zolda, E. 1998. Structural analysis of paintings based on brush strokes. In Proc. of SPIE Scientific Detection of Fakery in Art. SPIE.

Shen, C.; Kim, J.; Wang, L.; and van den Hengel, A. 2012. Positive semidefinite metric learning using boosting-like algorithms. Journal of Machine Learning Research 13:1007–1036.

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