Classification of traditional Chinese paintings using a modified embedding algorithm

14 March 2019 Classification of traditional Chinese paintings using a modified embedding algorithm

Jiachuan Sheng, Yuzhi Li

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), Science and Technology Development Strategy Research Project Funded by Tianjin Science and Technology Commission, Tianjin Natural Science Foundation

Journal of Electronic Imaging, Vol. 28, Issue 2, 023013 (March 2019). https://doi.org/10.1117/1.JEI.28.2.023013

Abstract

Although existing research on classification of Chinese paintings is limited to consideration of the relationship between paintings and labels, we propose a convolutional neural network (CNN)-based feature description, feature-weighted, and feature-prioritized algorithm to achieve overwhelmingly better classification performances. In comparison with the existing research on Chinese painting classifications, where the distribution information of paintings is often ignored and the influence of the feature importance on the calculation of distribution information is not considered, we extract the features of Chinese paintings by CNN models and propose a joint standard and normalized mutual information to allow features being prioritized via their level of importance. Following that, an embedded machine learning is further integrated to formulate an embedded classification algorithm, namely joint mutual information-based and data-embedded classification (JMIDEC), and the support vector machine is finally applied as the classifier to optimize the classification results. Extensive experiments show that the proposed JMIDEC algorithm outperforms a number of representative methods with stronger robustness.

Citation Download Citation

Jiachuan Sheng and Yuzhi Li "Classification of traditional Chinese paintings using a modified embedding algorithm," Journal of Electronic Imaging 28(2), 023013 (14 March 2019). https://doi.org/10.1117/1.JEI.28.2.023013

Received: 16 November 2018; Accepted: 19 February 2019; Published: 14 March 2019

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