Semisupervised multiview distance metric learning for cartoon synthesis
In image processing, cartoon character classification, retrieval, and synthesis are critical, so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features that comprehensively repre...
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ftxiamenuniv:oai:dspace.xmu.edu.cn:2288/92826 2023-05-15T16:01:15+02:00 Semisupervised multiview distance metric learning for cartoon synthesis Yu, Jun Wang, Meng Tao, Dacheng 俞俊 2012 http://dspace.xmu.edu.cn/handle/2288/92826 en_US eng Institute of Electrical and Electronics Engineers Inc. IEEE Transactions on Image Processing, 2012,21(11):4636-4648 1057-7149 20124415619792 http://dspace.xmu.edu.cn/handle/2288/92826 http://dx.doi.org/10.1109/TIP.2012.2207395 Algorithms Classification (of information) Image processing Image retrieval Synthesis (chemical) Article 2012 ftxiamenuniv 2020-07-21T11:46:04Z In image processing, cartoon character classification, retrieval, and synthesis are critical, so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters. In this paper, we introduce three visual features, color histogram, shape context, and skeleton, to characterize the color, shape, and action, respectively, of a cartoon character. These three features are complementary to each other, and each feature set is regarded as a single view. However, it is improper to concatenate these three features into a long vector, because they have different physical properties, and simply concatenating them into a high-dimensional feature vector will suffer from the so-called curse of dimensionality. Hence, we propose a semisupervised multiview distance metric learning (SSM-DML). SSM-DML learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously, under the umbrella of graph-based semisupervised learning. SSM-DML discovers complementary characteristics of different feature sets through an alternating optimization-based iterative algorithm. Therefore, SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement. On the basis of SSM-DML, we develop a novel system that composes the modules of multiview cartoon character classification, multiview graph-based cartoon synthesis, and multiview retrieval-based cartoon synthesis. Experimental evaluations based on the three modules suggest the effectiveness of SSM-DML in cartoon applications. 漏 2012 IEEE. Article in Journal/Newspaper DML Xiamen University Institutional Repository |
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ftxiamenuniv |
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English |
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Algorithms Classification (of information) Image processing Image retrieval Synthesis (chemical) |
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Algorithms Classification (of information) Image processing Image retrieval Synthesis (chemical) Yu, Jun Wang, Meng Tao, Dacheng 俞俊 Semisupervised multiview distance metric learning for cartoon synthesis |
topic_facet |
Algorithms Classification (of information) Image processing Image retrieval Synthesis (chemical) |
description |
In image processing, cartoon character classification, retrieval, and synthesis are critical, so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters. In this paper, we introduce three visual features, color histogram, shape context, and skeleton, to characterize the color, shape, and action, respectively, of a cartoon character. These three features are complementary to each other, and each feature set is regarded as a single view. However, it is improper to concatenate these three features into a long vector, because they have different physical properties, and simply concatenating them into a high-dimensional feature vector will suffer from the so-called curse of dimensionality. Hence, we propose a semisupervised multiview distance metric learning (SSM-DML). SSM-DML learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously, under the umbrella of graph-based semisupervised learning. SSM-DML discovers complementary characteristics of different feature sets through an alternating optimization-based iterative algorithm. Therefore, SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement. On the basis of SSM-DML, we develop a novel system that composes the modules of multiview cartoon character classification, multiview graph-based cartoon synthesis, and multiview retrieval-based cartoon synthesis. Experimental evaluations based on the three modules suggest the effectiveness of SSM-DML in cartoon applications. 漏 2012 IEEE. |
format |
Article in Journal/Newspaper |
author |
Yu, Jun Wang, Meng Tao, Dacheng 俞俊 |
author_facet |
Yu, Jun Wang, Meng Tao, Dacheng 俞俊 |
author_sort |
Yu, Jun |
title |
Semisupervised multiview distance metric learning for cartoon synthesis |
title_short |
Semisupervised multiview distance metric learning for cartoon synthesis |
title_full |
Semisupervised multiview distance metric learning for cartoon synthesis |
title_fullStr |
Semisupervised multiview distance metric learning for cartoon synthesis |
title_full_unstemmed |
Semisupervised multiview distance metric learning for cartoon synthesis |
title_sort |
semisupervised multiview distance metric learning for cartoon synthesis |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2012 |
url |
http://dspace.xmu.edu.cn/handle/2288/92826 |
genre |
DML |
genre_facet |
DML |
op_source |
http://dx.doi.org/10.1109/TIP.2012.2207395 |
op_relation |
IEEE Transactions on Image Processing, 2012,21(11):4636-4648 1057-7149 20124415619792 http://dspace.xmu.edu.cn/handle/2288/92826 |
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1766397192409448448 |