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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ftunivtsydney:oai:opus.lib.uts.edu.au:10453/22883 2023-05-15T16:01:15+02:00 Semisupervised multiview distance metric learning for cartoon synthesis Yu, J Wang, M Tao, D 2012-10-30 application/pdf http://hdl.handle.net/10453/22883 unknown IEEE Transactions on Image Processing 10.1109/TIP.2012.2207395 IEEE Transactions on Image Processing, 2012, 21 (11), pp. 4636 - 4648 1057-7149 http://hdl.handle.net/10453/22883 Artificial Intelligence & Image Processing Journal Article 2012 ftunivtsydney 2022-03-13T13:18:45Z 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. © 2012 IEEE. Article in Journal/Newspaper DML University of Technology Sydney: OPUS - Open Publications of UTS Scholars |
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University of Technology Sydney: OPUS - Open Publications of UTS Scholars |
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Artificial Intelligence & Image Processing |
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Artificial Intelligence & Image Processing Yu, J Wang, M Tao, D Semisupervised multiview distance metric learning for cartoon synthesis |
topic_facet |
Artificial Intelligence & Image Processing |
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. © 2012 IEEE. |
format |
Article in Journal/Newspaper |
author |
Yu, J Wang, M Tao, D |
author_facet |
Yu, J Wang, M Tao, D |
author_sort |
Yu, J |
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 |
publishDate |
2012 |
url |
http://hdl.handle.net/10453/22883 |
genre |
DML |
genre_facet |
DML |
op_relation |
IEEE Transactions on Image Processing 10.1109/TIP.2012.2207395 IEEE Transactions on Image Processing, 2012, 21 (11), pp. 4636 - 4648 1057-7149 http://hdl.handle.net/10453/22883 |
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1766397192763867136 |