Recognizing Cartoon Image Gestures for Retrieval and Interactive Cartoon Clip Synthesis

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 20, Issue 12, 2010, Pages 1745-1756

  Yang, Yi ^a   Zhuang, Yueting ^a   Tao, Dacheng ^b   Xu, Dong ^c   Yu, Jun ^c   Luo, Jiebo ^d  

^a ZHEJIANG UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^d EASTMAN KODAK COMPANY   (United States)

Author keywords

Cartoon clip synthesis; cartoon gesture recognition; content based cartoon image retrieval

Indexed keywords

CARTOON CLIP SYNTHESIS; CARTOON GESTURE RECOGNITION; CARTOON IMAGES; COLOR HISTOGRAM; CONTENT-BASED; EDGE FEATURES; LOCAL COLOR; LOCAL STRUCTURE; MOTION DIRECTION; SPECTRAL METHODS; TRANSDUCTIVE LEARNING;

GRAPHIC METHODS; IMAGE RETRIEVAL; LEARNING ALGORITHMS; SPECTROSCOPY;

GESTURE RECOGNITION;

EID: 79551593671     PISSN: 10518215     EISSN: 15582205     Source Type: Journal    
DOI: 10.1109/TCSVT.2010.2087452     Document Type: Article

References (55)

1. M. Haseyama and A. Matsumura, “A trainable retrieval system for cartoon character images,” in Proc. ICME, Jul. 2003, pp. 393–396.
2. A. Kort, “Computer aided inbetweening,” in Proc. Int. Symp. Non-Photorealistic Animation Rendering, 2002, pp. 125–132.
3. Y. Yang, D. Xu, F. Nie, S. Yan, and Y. Zhuang, “Image clustering using local discriminant models and global integration,” IEEE Trans. Image Process., vol. 19, no. 10, pp. 2761–2773, Oct. 2010.
4. Y. Zhuang, J. Yu, J. Xiao, and C. Chen, “Perspective-aware cartoon clips synthesis,” Comput. Animation Virtual Worlds, vol. 19, no. 3, pp. 355–364, 2008.
5. C. D. Juan and B. Bodenheimer, “Cartoon textures,” in Proc. Symp. Comput. Animation, 2004, pp. 267–276.
6. F. Cutzu, R. Hammoud, and A. Leykin, “Estimating the photorealism of images: Distinguishing paintings from photographs,” in Proc. CVPR, Jun. 2003, pp. 305–312.
7. D. Huttenlocher, G. Klanderman, and W. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 15, no. 9, pp. 850–863, Sep. 1993.
8. Y. Rubner, C. Tomasi, and L. Guibas, “A metric for distributions with applications to image databases,” in Proc. ICCV, 1998, p. 59.
9. X. Gao, B. Xiao, D. Tao, and X. Li, “Image categorization: Graph edit distance + edge direction histogram,” Patt. Recog., vol. 41, no. 10, pp. 3179–3191, Oct. 2008.
10. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face recognition using Laplacianfaces,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 27, no. 3, pp. 328–340, Mar. 2005.
11. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schölkopf, “Ranking on data manifolds,” in Proc. NIPS, 2003, pp. 169–176.
12. Y. Zhuang, Y. Yang, and F. Wu, “Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval,” IEEE Trans. Multimedia, vol. 10, no. 2, pp. 221–229, Feb. 2008.
13. J. He, M. Li, H. Zhang, H. Tong, and C. Zhang, “Manifold ranking based image retrieval,” in Proc. ACM MM, 2004, pp. 9–16.
14. M. Belkin and P. Niyogi, “Laplacian Eigenmaps and spectral techniques for embedding and clustering,” in Proc. NIPS, 2001, pp. 585–591.
15. T. Zhang, D. Tao, and J. Yang, “Discriminative locality alignment,” in Proc. ECCV, 2008, pp. 725–738.
16. Z. Zhang and H. Zha, “Principal manifolds and nonlinear dimension reduction via local tangent space alignment,” SIAM J. Scientific Comput., vol. 26, no. 1, pp. 313–338, 2005.
17. J. Tenenbaum, V. Silva, and J. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science, vol. 290, no. 5500, pp. 2319–2323, Dec. 2000.
18. H. Hotelling, “Analysis of a complex of statistical variables into principal components,” J. Educ. Psychol, vol. 24, no. 6, pp. 417–441, Sep. 1933.
19. S. Xiang, F. Nie, C. Zhang, and C. Zhang, “Nonlinear dimensionality reduction with local spline embedding,” IEEE Trans. Knowledge Data Eng., vol. 21, no. 9, pp. 1285–1298, Sep. 2009.
20. M. Haseyama and A. Matsumura, “A cartoon character retrieval system including trainable scheme,” in Proc. ICIP, Sep. 2003, pp. 37–40.
21. J. Fekete, E. Bizouarn, E. Cournarie, T. Galas, and F. Taillefer, “TicTacToon: A paperless system for professional 2-D animation,” in Proc. Conf. Comput. Graphics Interactive Tech., 1995, pp. 79–90.
22. S. Mitra and T. Acharya, “Gesture recognition: A survey,” IEEE Trans. Syst., Man, Cybern., Part C, vol. 37, no. 3, pp. 311–324, May 2007.
23. A. Bissacco, M. Yang, and S. Soatto, “Fast human pose estimation using appearance and motion via multi-dimensional boosting regression,” in Proc. CVPR, Jun. 2007, pp. 1–8.
24. M. Yang, N. Ahuja, and M. Tabb, “Extraction of 2-D motion trajectories and its application to hand gesture recognition,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 24, no. 8, pp. 1061–1074, Aug. 2002.
25. H. Chen, N. Zheng, L. Liang, Y. Li, Y. Xu, and H. Shum, “PicToon: A personalized image-based cartoon system,” in Proc. ACM Int. Conf. Multimedia, 2002, pp. 171–178.
26. Y. Li, F. Yu, Y. Xu, E. Chang, and H. Shum, “Speech-driven cartoon animation with emotions,” in Proc. ACM Multimedia, 2001, pp. 365–371.
27. L. Saul, S. Roweis, and Y. Singer, “Think globally, fit locally: Unsupervised learning of low dimensional manifolds,” J. Mach. Learn. Res., vol. 4, pp. 119–155, Dec. 2003.
28. J. Kruskal and M. Wish, Multidimensional Scaling. Beverly Hills, CA: Sage, 1977.
29. M. Ahmad and S. Lee, “Human action recognition using shape and CLG-motion flow from multi-view image sequences,” Patt. Recog., vol. 41, no. 7, pp. 2237–2252, 2008.
30. Y. Yang, D. Xu, F. Nie, J. Luo, and Y. Zhuang, “Ranking with local regression and global alignment for cross media retrieval,” in Proc. ACM MM, 2009, pp. 175–184.
31. H. Liu, T. Hong, M. Herman, T. Camus, and R. Chellappa, “Accuracy vs. efficiency trade-offs in optical flow algorithms,” Comput. Vision Image Understanding, vol. 72, no. 3, pp. 271–286, Dec. 1998.
32. A. Schödl, R. Szeliski, D. H. Salesin, and I. Essa, “Video textures,” in Proc. SIGGRAPH, 2000, pp. 489–498.
33. M. Lew, N. Sebe, C. Djeraba, and R. Jain, “Content-based multimedia information retrieval: State-of-the-art and challenges,” ACM Trans. Multimedia Comput., Commun., Applicat., vol. 2, no. 1, pp. 1–19, 2006.
34. F. Nie, S. Xiang, and C. Zhang, “Neighborhood MinMax projections,” in Proc. IJCAI, 2007, pp. 993–998.
35. Y. Liu, D. Zhang, G. Lu, and W. Ma, “A survey of content-based image retrieval with high-level semantics,” Patt. Recog., vol. 40, no. 1, pp. 262–282, 2007.
36. Y. Yang, Y. Zhuang, F. Wu, and Y. Pan, “Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval,” IEEE Trans. Multimedia, vol. 10, no. 3, pp. 437–446, Apr. 2008.
37. A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-based image retrieval at the end of the early years,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 22, no. 12, pp. 1349–1380, Dec. 2000.
38. D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vision, vol. 60, no. 2, pp. 91–110, 2004.
39. R. Zhang and Z. Zhang, “Effective image retrieval based on hidden concept discovery in image database,” IEEE Trans. Image Process., vol. 16, no. 2, pp. 562–572, Feb. 2006.
40. W. Bian and D. Tao, “Biased discriminant Euclidean embedding for content based image retrieval,” IEEE Trans. Image Process., vol. 19, no. 2, pp. 545–554, Feb. 2010.
41. D. Tao, X. Li, X. Wu, and S. Maybank, “Geometric mean for subspace selection,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 31, no. 2, pp. 260–274, Feb. 2009.
42. D. Tao, M. Song, X. Li, J. Shen, J. Sun, X. Wu, F. Christos, and S. Maybank, “Bayesian tensor approach for 3-D face modeling,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 10, pp. 1397–1410, Oct. 2008.
43. J. Kim and M. Song, “3-D gesture recognition using PCA of stereo images and modified matching algorithm,” in Proc. Int. Conf. Fuzzy Syst. Knowledge Dis., 2008, pp. 116–120.
44. H. Wu and A. Sutherland, “Dynamic gesture recognition using PCA with multi-scale theory and HMM,” in Proc. SPIE Int. Symp. Multispectral Image Process. Patt. Recog., 2001, pp. 132–139.
45. P. Gehler and S. Nowozin, “On feature combination for multiclass object classification,” in Proc. ICCV, 2009, pp. 221–228.
46. D. Hoiem, A. Efros, and M. Hebert, “Putting objects in perspective,” Int. J. Comput. Vision, vol. 80, no. 1, pp. 3–15, 2008.
47. Y. Li, T. Wang, and H. Shum, “Motion texture: A two-level statistical model for character motion synthesis,” in Proc. SIGGRAPH, 2002, pp. 465–472.
48. G. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. Jordan, “Learning the kernel matrix with semi-definite programming,” J. Mach. Learn. Res., vol. 5, pp. 27–72, Dec. 2004.
49. A. Kumar and C. Sminchisescu, “Support kernel machines for object recognition,” in Proc. ICCV, Oct. 2007, pp. 1–8.
50. A. Kapoor, H. I. Ahn, and R. W. Picard, “Mixture of Gaussian processes for combining multiple modalities,” in Proc. 6th Int. Workshop MCS, Jun. 2005, pp. 86–96.
51. Y. Yang, Y. Zhuang, D. Xu, Y. Pan, D. Tao, and S. Maybank, “Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning,” in Proc. ACM Multimedia, 2009, pp. 311–320.
52. J. Shen, D. Tao, and X. Li, “Modality mixture projections for semantic video event detection,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 11, pp. 1587–1596, Nov. 2008.
53. Y. Yang, F. Nie, S. Xiang, Y. Zhuang, and W. Wang, “Local and global regressive mapping for manifold learning with out-of-sample extrapolation,” in Proc. AAAI, 2010, pp. 649–654.
54. S. Xiang, F. Nie, C. Zhang, and C. Zhang, “Interactive natural image segmentation via spline regression,” IEEE Trans. Image Process., vol. 18, no. 7, pp. 1623–1632, Jul. 2009.
55. T. Lee, C. Lin, Y. Wang, and T. Chen, “Animation key-frame extraction and simplification using deformation analysis,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 4, pp. 478–486, Apr. 2008.