Title:
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REPEATED PATTERN EXTRACTION
WITH KNOWLEDGE-BASED ATTENTION
AND SEMANTIC EMBEDDINGS |
Author(s):
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Hong Qu, Yanghong Zhou, K.P. Chau and P.Y. Mok |
ISBN:
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978-989-8704-21-4 |
Editors:
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Yingcai Xiao, Ajith P. Abraham and Jörg Roth |
Year:
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2020 |
Edition:
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Single |
Keywords:
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Repeated Pattern Extraction, Boundary Detection, CNN, Template Matching, Textile Design, AlexNet |
Type:
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Full |
First Page:
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99 |
Last Page:
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106 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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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 AlexNets 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. |
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