A CNN-LSTM FRAMEWORK FOR AUTHORSHIP CLASSIFICATION OF PAINTINGS

被引：0

作者：

Jangtjik, Kevin Alfianto ^{[1
]}

Trang-Thi Ho ^{[1
]}

Yeh, Mei-Chen ^{[2
]}

Hua, Kai-Lung ^{[1
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept CSIE, Taipei, Taiwan

[2] Natl Taiwan Normal Univ, Dept CSIE, Taipei, Taiwan

来源：

2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2017年

关键词：

Digital image classification; multiscale pyramid representation; convolutional neural network; long short-term memory networks;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

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.

引用

页码：2866 / 2870

页数：5