TY - GEN
T1 - A CNN-LSTM framework for authorship classification of paintings
AU - Jangtjik, Kevin Alfianto
AU - Ho, Trang Thi
AU - Yeh, Mei Chen
AU - Hua, Kai Lung
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The authenticity of digital painting image is an urgent demand in the field of art. Yet, determining the authorship of a certain painting is a challenging task due to two reasons: (1) various artists might share similar painting styles; and (2) an artist could create different styles. In this paper, we present a novel method for authorship classification of paintings based on a CNN-LSTM framework. First, a multiscale pyramid is constructed from a painting image. Second, a CNN-LSTM model is learned and it returns possibly multiple labels for one image. To aggregate the final classification result, an adaptive fusion method is employed. Experimental results show that the proposed method has superior classification performance compared with the state-of-the-art techniques.
AB - The authenticity of digital painting image is an urgent demand in the field of art. Yet, determining the authorship of a certain painting is a challenging task due to two reasons: (1) various artists might share similar painting styles; and (2) an artist could create different styles. In this paper, we present a novel method for authorship classification of paintings based on a CNN-LSTM framework. First, a multiscale pyramid is constructed from a painting image. Second, a CNN-LSTM model is learned and it returns possibly multiple labels for one image. To aggregate the final classification result, an adaptive fusion method is employed. Experimental results show that the proposed method has superior classification performance compared with the state-of-the-art techniques.
KW - Convolutional neural network
KW - Digital image classification
KW - Long short-term memory networks
KW - Multiscale pyramid representation
UR - http://www.scopus.com/inward/record.url?scp=85045346024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045346024&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2017.8296806
DO - 10.1109/ICIP.2017.8296806
M3 - Conference contribution
AN - SCOPUS:85045346024
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2866
EP - 2870
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Image Processing, ICIP 2017
Y2 - 17 September 2017 through 20 September 2017
ER -