Knowledge discovery of artistic influences: A metric learning approach

Babak Saleh, Kanako Abe, Ahmed Elgammal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We approach the challenging problem of discovering influences between painters based on their fine-art paintings. In this work, we focus on comparing paintings of two painters in terms of visual similarity. This comparison is fully automatic and based on computer vision approaches and machine learning. We investigated different 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th International Conference on Computational Creativity, ICCC 2014
EditorsSimon Colton, Dan Ventura, Nada Lavrac, Michael Cook
PublisherJozef Stefan Institute
ISBN (Electronic)9789612640552
StatePublished - 2014
Event5th International Conference on Computational Creativity, ICCC 2014 - Ljubljana, Slovenia
Duration: Jun 10 2014Jun 13 2014

Publication series

NameProceedings of the 5th International Conference on Computational Creativity, ICCC 2014

Conference

Conference5th International Conference on Computational Creativity, ICCC 2014
CountrySlovenia
CityLjubljana
Period6/10/146/13/14

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics

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