TY - GEN
T1 - Classifying paintings by artistic genre
T2 - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
AU - Zujovic, Jana
AU - Gandy, Lisa
AU - Friedman, Scott
AU - Pardo, Bryan A
AU - Pappas, Thrasyvoulos N
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=74349113528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74349113528&partnerID=8YFLogxK
U2 - 10.1109/MMSP.2009.5293271
DO - 10.1109/MMSP.2009.5293271
M3 - Conference contribution
AN - SCOPUS:74349113528
SN - 9781424444649
T3 - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
BT - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
Y2 - 5 October 2009 through 7 October 2009
ER -