Classifying paintings by artistic genre: An analysis of features & classifiers

Jana Zujovic, Lisa Gandy, Scott Friedman, Bryan Pardo, Thrasyvoulos N. Pappas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

75 Scopus citations

Abstract

This paper describes an approach to automatically classify digital pictures of paintings by artistic genre. While the task of artistic classification is often entrusted to human experts, recent advances in machine learning and multimedia feature extraction has made this task easier to automate. Automatic classification is useful for organizing large digital collections, for automatic artistic recommendation, and even for mobile capture and identification by consumers. Our evaluation uses variable-resolution painting data gathered across Internet sources rather than solely using professional high-resolution data. Consequently, we believe this solution better addresses the task of classifying consumer-quality digital captures than other existing approaches. We include a comparison to existing feature extraction and classification methods as well as an analysis of our own approach across classifiers and feature vectors.

Original languageEnglish
Title of host publication2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
DOIs
StatePublished - 2009
Event2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09 - Rio De Janeiro, Brazil
Duration: Oct 5 2009Oct 7 2009

Publication series

Name2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09

Conference

Conference2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
Country/TerritoryBrazil
CityRio De Janeiro
Period10/5/0910/7/09

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