| 摘要(英) |
During the past few years, the use of machine learning in the field of computer science has become a subject of extensive discussion. Through learning features from training data, creative intelligence that gives machines ability to creation has been developed in many areas such as literature, industrial design, music, and images.
This thesis focuses on automatic creation of cartoon images, based on the input training examples. Unsupervised learning methods are applied to analyze the input training images. We use image segmentation to obtain regions. Then characteristics of regions are calculated. For each input image, we establish a connected region relationship graph to describe the relationship between the adjacent regions. All the regions from example images are clustered according to their characteristics. After clustering, the clustering refinement step is designed using the statistics of each cluster. Finally, the new images are created according to the results of clustering.
The experimental results show that the system proposed in this thesis can effectively segment the area of the training examples and provide good clustering results after clustering refinement. As the result of the creation of the image is subjective, we perform subjective tests on different users to score our creation results. The results are satisfying and the proposed system is computationally fast. |
| 參考文獻 |
[1] Liu, Chien-Hung, and Chuan-Kang Ting. 2015. “Music Pattern Mining for Chromosome Representation in Evolutionary Composition.” In IEEE Congress on Evolutionary Computation (CEC 2015), Sendai, Japan, 25–28 May 2015, 2145–2152.
[2] Mordvintsev, Alexander, Olah, Christopher and Tyka, Mike, “DeepDream - a code example for visualizing Neural Networks”; Google Research (2015)
[3] Mordvintsev, Alexander, Olah, Christopher and Tyka, Mike,”Inceptionism: Going Deeper into Neural Networks”; Google Research (2015)
[4] Mordvintsev, Alexander, Olah, Christopher and Tyka, Mike, ”Inceptionism: Going Deeper into Neural Networks”; Google Research (2015)
[5] Leon A. Gatys, Alexander S. Ecker and Matthias Bethge,” A Neural Algorithm of Artistic Style”; arXiv preprint arXiv:1508.06576 (2015)
[6] Leon A. Gatys, Alexander S. Ecker and Matthias Bethge,” Image Style Transfer Using Convolutional Neural Networks”; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2414-2423
[7] Szegedy hristian, Liu Wei, Jia Yangqing, Sermanet Pierre, Reed Scott, Anguelov Dragomir, Erhan Dumitru, Vanhoucke Vincent and Rabinovich Andrew, “Going Deeper with Convolutions”; Computing Research Repository. arXiv:1409.4842 (2014)
[8] R. Kimmel and A.M. Bruckstein,”On regularized Laplacian zero crossings and other optimal edge integrators”; International Journal of Computer Vision, 53(3) pages 225–243(2003)
[9] Nock, R. and F. Nielsen, Statistical region merging. IEEE Transactions on pattern analysis and machine intelligence, 2004. 26(11): p. 1452-1458.
[10] Samet, H.; Tamminen, M. (1988). "Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees". IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Michael B. Dillencourt; Hannan Samet; Markku Tamminen (1992). "A general approach to connected-component labeling for arbitrary image representations". Journal of the ACM.
[12] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein], Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN 0-262-03293-7. Section 22.2: Breadth-first search, pp. 531–539.
[13] MacQueen, J. B. (1967). Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. 1. University of California Press. pp. 281–297.
[14] Arthur, D.; Vassilvitskii, S. (2007). "k-means++: the advantages of careful seeding" (PDF). Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics Philadelphia, PA, USA. pp. 1027–1035.
[15] Canny, J., A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, 1986(6): p. 679-698.
[16] Dan Motzenbecker and Kyle Phillips, “Google AutoDraw”, https://www.autodraw.com/
[17] Wei-Ta Chu and Chia-Hsiang Yu, "Optimized Speech Balloon Placement for Automatic Comics Generation," Proceedings of ACM International Workshop on Interactive Multimedia on Mobile and Portable Devices, pp. 1-6, 2013. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit