Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification

Li Liu; Wenyan Cheng; Taorong Qiu; Chengying Tao; Qiu Chen; Yue Lu; Ching Y. Suen

doi:10.1007/978-3-030-92310-5_49

Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification

Li Liu^*, Wenyan Cheng, Taorong Qiu, Chengying Tao, Qiu Chen, Yue Lu, Ching Y. Suen

^*Corresponding author for this work

School of Communication and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

We tackle the problem of Chinese calligraphy style classification in this paper, which is an important concern in the field of calligraphy art. The subtle difference among different calligraphy styles makes style classification a very challenging problem. To solve this problem, we propose a multi-loss siamese convolutional neural network, which is composed of two streams sharing weights. Each stream accepts a distinct image and then employs a convolutional neural network for feature extraction. We adopt the contrastive loss to explicitly enforce that the distance between the features of the images from the same category is smaller than that between the features of the images from different categories. Moreover, each stream of the siamese network is extended with a classification subnetwork to fully exploit the supervised information of an individual image. The cross-entropy loss is then employed for the classification subnetwork. By jointly optimizing the two types of loss, the proposed network has obtained remarkable performance according to the extensive experiments, achieving an accuracy in excess of 98 %.

Original language	English
Title of host publication	Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
Editors	Teddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	425-432
Number of pages	8
ISBN (Print)	9783030923099
DOIs	https://doi.org/10.1007/978-3-030-92310-5_49
State	Published - 2021
Event	28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online Duration: 8 Dec 2021 → 12 Dec 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1517 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	28th International Conference on Neural Information Processing, ICONIP 2021
City	Virtual, Online
Period	8/12/21 → 12/12/21

Keywords

Chinese calligraphy style classification
Contrastive loss
Cross-entropy loss
Multi-loss siamese convolutional neural network

Access to Document

10.1007/978-3-030-92310-5_49

Cite this

Liu, L., Cheng, W., Qiu, T., Tao, C., Chen, Q., Lu, Y., & Suen, C. Y. (2021). Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification. In T. Mantoro, M. Lee, M. A. Ayu, K. W. Wong, & A. N. Hidayanto (Eds.), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings (pp. 425-432). (Communications in Computer and Information Science; Vol. 1517 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-92310-5_49

Liu, Li ; Cheng, Wenyan ; Qiu, Taorong et al. / Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification. Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. editor / Teddy Mantoro ; Minho Lee ; Media Anugerah Ayu ; Kok Wai Wong ; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. pp. 425-432 (Communications in Computer and Information Science).

@inproceedings{922eeed706914a929639dc364a552e2c,

title = "Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification",

abstract = "We tackle the problem of Chinese calligraphy style classification in this paper, which is an important concern in the field of calligraphy art. The subtle difference among different calligraphy styles makes style classification a very challenging problem. To solve this problem, we propose a multi-loss siamese convolutional neural network, which is composed of two streams sharing weights. Each stream accepts a distinct image and then employs a convolutional neural network for feature extraction. We adopt the contrastive loss to explicitly enforce that the distance between the features of the images from the same category is smaller than that between the features of the images from different categories. Moreover, each stream of the siamese network is extended with a classification subnetwork to fully exploit the supervised information of an individual image. The cross-entropy loss is then employed for the classification subnetwork. By jointly optimizing the two types of loss, the proposed network has obtained remarkable performance according to the extensive experiments, achieving an accuracy in excess of 98 \%.",

keywords = "Chinese calligraphy style classification, Contrastive loss, Cross-entropy loss, Multi-loss siamese convolutional neural network",

author = "Li Liu and Wenyan Cheng and Taorong Qiu and Chengying Tao and Qiu Chen and Yue Lu and Suen, \{Ching Y.\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1007/978-3-030-92310-5\_49",

language = "英语",

isbn = "9783030923099",

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editor = "Teddy Mantoro and Minho Lee and Ayu, \{Media Anugerah\} and Wong, \{Kok Wai\} and Hidayanto, \{Achmad Nizar\}",

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Liu, L, Cheng, W, Qiu, T, Tao, C, Chen, Q, Lu, Y & Suen, CY 2021, Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification. in T Mantoro, M Lee, MA Ayu, KW Wong & AN Hidayanto (eds), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Communications in Computer and Information Science, vol. 1517 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 425-432, 28th International Conference on Neural Information Processing, ICONIP 2021, Virtual, Online, 8/12/21. https://doi.org/10.1007/978-3-030-92310-5_49

Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification. / Liu, Li; Cheng, Wenyan; Qiu, Taorong et al.
Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. ed. / Teddy Mantoro; Minho Lee; Media Anugerah Ayu; Kok Wai Wong; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. p. 425-432 (Communications in Computer and Information Science; Vol. 1517 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We tackle the problem of Chinese calligraphy style classification in this paper, which is an important concern in the field of calligraphy art. The subtle difference among different calligraphy styles makes style classification a very challenging problem. To solve this problem, we propose a multi-loss siamese convolutional neural network, which is composed of two streams sharing weights. Each stream accepts a distinct image and then employs a convolutional neural network for feature extraction. We adopt the contrastive loss to explicitly enforce that the distance between the features of the images from the same category is smaller than that between the features of the images from different categories. Moreover, each stream of the siamese network is extended with a classification subnetwork to fully exploit the supervised information of an individual image. The cross-entropy loss is then employed for the classification subnetwork. By jointly optimizing the two types of loss, the proposed network has obtained remarkable performance according to the extensive experiments, achieving an accuracy in excess of 98 %.

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Liu L, Cheng W, Qiu T, Tao C, Chen Q, Lu Y et al. Multi-loss Siamese Convolutional Neural Network for Chinese Calligraphy Style Classification. In Mantoro T, Lee M, Ayu MA, Wong KW, Hidayanto AN, editors, Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 425-432. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-92310-5_49