TY - GEN
T1 - Color multi-fusion fisher vector feature for fine art painting categorization and influence analysis
AU - Puthenputhussery, Ajit
AU - Liu, Qingfeng
AU - Liu, Chengjun
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/23
Y1 - 2016/5/23
N2 - This paper presents a novel set of image features that encode the local, color, spatial, relative intensity information and gradient orientation of the painting image for painting artist classification, style classification as well as artist and style influence analysis. In particular, a new color DAISY Fisher vector (CD-FV) feature is first created by computing Fisher vectors on densely sampled DAISY features. Second, a color WLD-SIFT Fisher vector (CWS-FV) feature is developed by fusing Weber local descriptors (WLD) with Scale Invariant Feature Transform (SIFT) descriptors and Fisher vectors are computed on the fused WLD-SIFT features. Finally, an innovative color multi-fusion Fisher vector (CMFFV) feature is developed by integrating the Principal Component Analysis (PCA) features of CD-FV, CWS-FV and color SIFT-FV features. The effectiveness of the proposed CMFFV feature is assessed on the challenging Painting-91 dataset. Experimental results show that the proposed CMFFV feature is able to (i) achieve the state-of-the-art performance for painting artist classification, (ii) outperform other popular image descriptors, as well as (iii) discover the artist and style influence to understand their connections and evolution in different art movement periods.
AB - This paper presents a novel set of image features that encode the local, color, spatial, relative intensity information and gradient orientation of the painting image for painting artist classification, style classification as well as artist and style influence analysis. In particular, a new color DAISY Fisher vector (CD-FV) feature is first created by computing Fisher vectors on densely sampled DAISY features. Second, a color WLD-SIFT Fisher vector (CWS-FV) feature is developed by fusing Weber local descriptors (WLD) with Scale Invariant Feature Transform (SIFT) descriptors and Fisher vectors are computed on the fused WLD-SIFT features. Finally, an innovative color multi-fusion Fisher vector (CMFFV) feature is developed by integrating the Principal Component Analysis (PCA) features of CD-FV, CWS-FV and color SIFT-FV features. The effectiveness of the proposed CMFFV feature is assessed on the challenging Painting-91 dataset. Experimental results show that the proposed CMFFV feature is able to (i) achieve the state-of-the-art performance for painting artist classification, (ii) outperform other popular image descriptors, as well as (iii) discover the artist and style influence to understand their connections and evolution in different art movement periods.
UR - http://www.scopus.com/inward/record.url?scp=84977616626&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977616626&partnerID=8YFLogxK
U2 - 10.1109/WACV.2016.7477619
DO - 10.1109/WACV.2016.7477619
M3 - Conference contribution
AN - SCOPUS:84977616626
T3 - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
BT - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Y2 - 7 March 2016 through 10 March 2016
ER -