TY - GEN
T1 - Repeated pattern extraction with knowledge-based attention and semantic embeddings
AU - Qu, Hong
AU - Zhou, Yanghong
AU - Chau, K. P.
AU - Mok, P. Y.
N1 - Funding Information:
The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 152161/17E and 152112/19E). The work was also partially supported by The Innovation and Technology Fund (Grant No. ITP/013/18TI) and Shenzhen Science and Technology Innovation Commission (Project No. JCYJ20170303160155330).
Publisher Copyright:
© Proceedings of the 14th IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS International Conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS International Conference Theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Repeat pattern is a common structure in textile or graphic design, while scalable and reusable designs are required for production. We propose a new approach, based on Convolutional Neural Network (CNN), for automatic extraction of repeat patterns in this paper. CNNs are good at detecting repeat patterns through extracting multilevel features in the convolutional layers. In our method, we select filters quickly in single images by leveraging the combination of the learned filters from AlexNet's convolutional layers and boundary detection results from VGG16. Moreover, we use template matching to optimize final outputs in order to improve precision. We composed a dataset with 30 images, covering stripe, check and dot distributed patterns, each image with manual ground truth labels. The experimental results show that our method outperforms the state-of-the-art method in both precision and running speeds.
AB - Repeat pattern is a common structure in textile or graphic design, while scalable and reusable designs are required for production. We propose a new approach, based on Convolutional Neural Network (CNN), for automatic extraction of repeat patterns in this paper. CNNs are good at detecting repeat patterns through extracting multilevel features in the convolutional layers. In our method, we select filters quickly in single images by leveraging the combination of the learned filters from AlexNet's convolutional layers and boundary detection results from VGG16. Moreover, we use template matching to optimize final outputs in order to improve precision. We composed a dataset with 30 images, covering stripe, check and dot distributed patterns, each image with manual ground truth labels. The experimental results show that our method outperforms the state-of-the-art method in both precision and running speeds.
KW - AlexNet
KW - Boundary detection
KW - CNN
KW - Repeated pattern extraction
KW - Template matching
KW - Textile design
UR - http://www.scopus.com/inward/record.url?scp=85101092576&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85101092576
T3 - Proceedings of the 14th IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS International Conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS International Conference Theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020
SP - 99
EP - 106
BT - Proceedings of the 14th IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS International Conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS International Conference Theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020
PB - IADIS Press
T2 - 14th IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and 5th IADIS International Conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and 9th IADIS International Conference Theory and Practice in Modern Computing 2020, TPMC 2020, Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020
Y2 - 23 July 2020 through 25 July 2020
ER -