Learning relations among movie characters: A social network perspective

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6314 LNCS, Issue PART 4, 2010, Pages 410-423

  Ding, Lei ^a   Yilmaz, Alper ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; SOCIAL NETWORKING (ONLINE); TECHNOLOGY TRANSFER;

AUDITORY CUES; GAUSSIAN PROCESSES; LEARNING RELATIONS; LOCAL PROPERTY; REASONABLE ACCURACY; STATISTICAL LEARNING; SUPPORT VECTOR REGRESSION (SVR); TELEVISION SHOWS;

MOTION PICTURES;

EID: 78149291035     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15561-1_30     Document Type: Conference Paper

References (24)

1. Ali, S., Basharat, A., Shah, M.: Chaotic invariants for human action recognition. In: ICCV (2007)
2. Alon, J., Athitsos, V., Yuan, Q., Sclaroff, S.: A unified framework for gesture recognition and spatiotemporal gesture segmentation. IEEE Trans. on PAMI 31(9), 1685-1699 (2009)
3. Arandjelović, O., Zisserman, A.: Automatic face recognition for film character retrieval in feature-length films. In: CIVR (2005)
4. Chen, J., Zaiane, O., Goebel, R.: Detecting communities in social networks using max-min modularity. In: SDM (2009)
5. Cour, T., Jordan, C., Miltsakaki, E., Taskar, B.: Movie/script: Alignment and parsingof video and text transcription. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 158-171. Springer, Heidelberg (2008)
6. Ding, L., Fan, Q., Hsiao, J., Pankanti, S.: Graph based event detection from realistic videos using weak feature correspondence. In: ICASSP (2010)
7. Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: ICCV (2003)
8. Fathi, A., Mori, G.: Action recognition by learning mid-level motion features. In: CVPR (2008)
9. Freeman, L.: Centrality in social networks: Conceptual clarification. Social Networks 1(3), 215-239 (1979)
10. Ge, W., Collins, R., Ruback, B.: Automatically detecting the small group structure of a crowd. In: WACV (2009)
11. Jiang, H., Fels, S., Little, J.: A linear programming approach for multiple object tracking. In: CVPR (2007)
12. Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV (2003)
13. Lin, J., Wang, W.: Weakly-supervised violence detection in movies with audio and video based co-training. In: PCM (2009)
14. Lu, Z., Carreira-Perpinan, M.A.: Constrained spectral clustering through affinity propagation. In: CVPR (2008)
15. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI (1981)
16. Merriam-Webster: Merriam-webster online dictionary (2010), http://www.merriam-webster.com/dictionary
17. Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577-8582 (2006)
18. Rasheed, Z., Shah, M.: Movie genre classification by exploiting audio-visual features of previews. In: ICPR (2002)
19. Ruhnau, B.: Eigenvector-centrality? a node-centrality. Social Networks 22(4), 357-365 (2000)
20. Shi, J., Tomasi, C.: Good features to track. In: CVPR (1994)
21. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14(3), 199-222 (2004)
22. Wasserman, S., Faust, K., Iacobucci, D.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
23. Yu, T., Lim, S.N., Patwardhan, K., Krahnstoever, N.: Monitoring, recognizing and discovering social networks. In: CVPR (2009)
24. Zhai, Y., Shah, M.: Video scene segmentation using markov chain monte carlo. IEEE Trans. on Multimedia 8(4), 686-697 (2006) (Pubitemid 44144154)