Title :
Rhythmic Brushstrokes Distinguish van Gogh from His Contemporaries: Findings via Automated Brushstroke Extraction
Author :
Li, Jia ; Yao, Lei ; Hendriks, Ella ; Wang, James Z.
Author_Institution :
Dept. of Stat., Pennsylvania State Univ., University Park, PA, USA
fDate :
6/1/2012 12:00:00 AM
Abstract :
Art historians have long observed the highly characteristic brushstroke styles of Vincent van Gogh and have relied on discerning these styles for authenticating and dating his works. In our work, we compared van Gogh with his contemporaries by statistically analyzing a massive set of automatically extracted brushstrokes. A novel extraction method is developed by exploiting an integration of edge detection and clustering-based segmentation. Evidence substantiates that van Gogh´s brushstrokes are strongly rhythmic. That is, regularly shaped brushstrokes are tightly arranged, creating a repetitive and patterned impression. We also found that the traits that distinguish van Gogh´s paintings in different time periods of his development are all different from those distinguishing van Gogh from his peers. This study confirms that the combined brushwork features identified as special to van Gogh are consistently held throughout his French periods of production (1886-1890).
Keywords :
art; edge detection; feature extraction; image segmentation; pattern clustering; statistical analysis; art historians; automated brushstroke extraction; clustering-based segmentation; edge detection; repetitive impression; rhythmic brushstrokes; time periods; van Gogh brushstrokes; van Gogh paintings; Art; Educational institutions; Electronic mail; Feature extraction; Image edge detection; Painting; Statistical analysis; Oil paintings; brushstroke style; postimpressionism; statistical analysis.; van Gogh;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2011.203