TY - GEN
T1 - DESIGN ELEMENTS EXTRACTION BASED ON UNSUPERVISED SEGMENTATION AND COMPACT VECTORIZATION
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).
Publisher Copyright:
© MCCSIS 2022.All rights reserved.
PY - 2022/7
Y1 - 2022/7
N2 - Repeated design elements are abundant and ubiquitous in decorative patterns, which are now widely used in the process of design for objects, artworks and images found in our living environment. Extraction of repeated design elements from images of existing decorative patterns benefits understanding design, extracting compressed information for subsequent operation, e.g., design generation and vectorization. The early methods based on hand-crafted features were computationally inefficient and less accurate. Most deep learning (DL) based methods focus on natural environment images and are difficult to generalize to decorative images. Besides, DL-based methods require massive datasets with instance-level annotations, which are labor-intensive and hard to get. This paper proposes a novel scheme for design element extraction and vectorization. First of all, unsupervised segmentation is proposed to extract repeated design elements from images of unknown artworks without human assistance. We then distill the color information of the extracted repeated element based on statistics reflected in the color histogram of the input artwork. We develop an algorithm to remove redundant information extracted from images in order to get a compact vectorization result, reusable design element in vector format, at the end. To validate the proposed scheme, we conducted several experiments and the result demonstrated the effectiveness of our scheme and its potential for design generation application.
AB - Repeated design elements are abundant and ubiquitous in decorative patterns, which are now widely used in the process of design for objects, artworks and images found in our living environment. Extraction of repeated design elements from images of existing decorative patterns benefits understanding design, extracting compressed information for subsequent operation, e.g., design generation and vectorization. The early methods based on hand-crafted features were computationally inefficient and less accurate. Most deep learning (DL) based methods focus on natural environment images and are difficult to generalize to decorative images. Besides, DL-based methods require massive datasets with instance-level annotations, which are labor-intensive and hard to get. This paper proposes a novel scheme for design element extraction and vectorization. First of all, unsupervised segmentation is proposed to extract repeated design elements from images of unknown artworks without human assistance. We then distill the color information of the extracted repeated element based on statistics reflected in the color histogram of the input artwork. We develop an algorithm to remove redundant information extracted from images in order to get a compact vectorization result, reusable design element in vector format, at the end. To validate the proposed scheme, we conducted several experiments and the result demonstrated the effectiveness of our scheme and its potential for design generation application.
KW - Image Understanding
KW - Localization
KW - Unsupervised Segmentation
KW - Vectorization
UR - http://www.scopus.com/inward/record.url?scp=85142370190&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85142370190
T3 - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
SP - 78
EP - 84
BT - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
PB - IADIS Press
T2 - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
Y2 - 19 July 2022 through 22 July 2022
ER -