Automatic traditional Chinese painting classification: A benchmarking analysis. (3rd May 2020)

• Record Type: Journal Article
• Authors: Liong, Sze‐Teng; Huang, Yen‐Chang; Li, Shumeng; Huang, Zhongkai; Ma, Jingyang; Gan, Yee Siang
• Is Part Of: Computational intelligence. Volume 36:Number 3(2020)
• Page Start: 1183
• Page End: 1199
• Publication Date: 2020-05-03
• Subjects: classification -- deep learning -- instance segmentation -- traditional Chinese painting; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12328
• Languages: English
• ISSNs: 0824-7935

Abstract

Summary: In the recent years, there is a growing trend toward digitization of cultural heritage for better accessibility and preservation. For instance, the development of image processing techniques in traditional Chinese painting (TCP) has begun to attract researchers' attention in the computer vision field. TCP is one of the representative of Chinese traditional arts. Evidenced by the successes of development in image processing techniques in various applications, this article aim to apply the deep learning approach on TCP for several purposes, which include automatic establishment of unified image library, facilitating update‐to‐date data in the database, reduction of cost required for image classification and retrieval. First, a unified database is established, that consists of more than a thousand of images from six major TCP themes. Then, several deep learning algorithms that are based on mathematical models are applied to examine the classification performance. In addition, the salient regions that denote significant features are identified, by adopting the instance segmentation technique. As a result, the modified pretrained neural network is capable to achieve 99.66% recognition accuracy. Qualitative results are also presented to demonstrate the effectiveness of the proposed method. We also note that this is the first work that performs multiclass classification on six categories in this domain. Furthermore, a 10‐class classification result of 96% is obtained when performing on one of the painting types, namely, ghost‐and‐god.