Small sample learning of calligraphy and painting identification based on hyperspectral image

Xingjia Tang; Pengchang Zhang; Zongben Xu; Bingliang Hu; Siyuan Li

doi:10.1117/12.2663778

4 April 2023 Small sample learning of calligraphy and painting identification based on hyperspectral image

Xingjia Tang, Pengchang Zhang, Zongben Xu, Bingliang Hu, Siyuan Li

Proceedings Volume 12617, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications; 126170Y (2023) https://doi.org/10.1117/12.2663778
Event: 9th Symposium on Novel Photoelectronic Detection Technology and Applications (NDTA 2022), 2022, Hefei, China

Abstract

The existence of forgeries has seriously affected the fair trading, protection and inheritance of calligraphy and painting, while it has been unable to identify high-level counterfeiting means by traditional expert eye identification method. Combining the advantages of material attribute recognition and imaging analysis of hyperspectral imaging technology with the powerful feature expression and classification ability of convolutional neural network, the identification level of calligraphy and painting could be improved. However, there are still some practical problems in the application, like the small sample learning problem caused by the difficulty in obtaining the real hyperspectral sample data of calligraphy and painting. In this paper, a 10-hidden layers 2D-CNN convolutional neural network transfer learning method for calligraphy and painting identification with data enhancement is proposed by using a large number of relevant picture data and a small amount of MNF dimensionality reduced hyperspectral data. The experimental test shows that on the test set of this paper, for the identification of calligraphy and painting authors and authenticity, the accuracy of migration learning with data enhancement under the original sample are separately 97.5% and 94.8%, the accuracy of migration learning with data enhancement under half of the original sample are separately 94.3% and 92.8%, which shows the migration learning and data enhancement is helpful, and the identification accuracy of half of the original sample basically reaches the identification accuracy of the original sample without data enhancement and transfer learning, whose accuracy are 92.1% and 92.5%.

Citation Download Citation

Xingjia Tang, Pengchang Zhang, Zongben Xu, Bingliang Hu, and Siyuan Li "Small sample learning of calligraphy and painting identification based on hyperspectral image", Proc. SPIE 12617, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications, 126170Y (4 April 2023); https://doi.org/10.1117/12.2663778

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Convolution

Hyperspectral imaging

Image enhancement

Statistical modeling

Convolutional neural networks

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years