Understanding image virality

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 1818-1826

  Deza, Arturo ^a   Parikh, Devi ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; FORECASTING; PATTERN RECOGNITION; SEMANTICS;

HIGH-LEVEL INFORMATION; HUMAN PERFORMANCE; LOW-LEVEL FEATURES; MACHINE PERFORMANCE; ON-LINE CONTENTS; SEMANTIC INFORMATION; STATE OF THE ART; VISUAL ATTRIBUTES;

COMPUTER VISION;

EID: 84959189740     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298791     Document Type: Conference Paper

References (38)

1. H. Agrawal, N. Chavali, M. C., Y. Goyal, A. Alfadda,, P. Banik., and D. Batra. Cloudcv: Large-scale distributed computer vision as a cloud service, 2013
2. A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 2005
3. A. Berg, T. Berg, H. Daume, J. Dodge, A. Goyal, X. Han, A. Mensch, M. Mitchell, A. Sood, K. Stratos, et al. Understanding and predicting importance in images. In CVPR, 2012
4. J. Berger. Arousal increases social transmission of information. Psychological science, 2011
5. J. Berger. Contagious: Why Things Catch On. Simon & Schuster, 2013
6. J. Berger and K. L. Milkman. What makes online content viral? Journal of Marketing Research, 2012
7. J. Berger and E. M. Schwartz. What drives immediate and ongoing word of mouth? Journal of Marketing Research, 2011
8. A. Beutel, B. A. Prakash, R. Rosenfeld, and C. Faloutsos. Interacting viruses in networks: can both survive? In SIGKDD, 2012
9. D. Bitouk, N. Kumar, S. Dhillon, P. Belhumeur, and S. K. Nayar. Face swapping: automatically replacing faces in photographs. In Transactions on Graphics (TOG), 2008
10. D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In Proceedings of the 21st ACM international conference on Multimedia, pages 223-232. ACM, 2013
11. Z. Chen and J. Berger. When, why, and how controversy causes conversation. The Wharton School Research Paper, 2012
12. E. J. Crowley and A. Zisserman. In search of art. In Workshop on Computer Vision for Art Analysis, ECCV, 2014
13. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005
14. S. Dhar, V. Ordonez, and T. L. Berg. High level describable attributes for predicting aesthetics and interestingness. In CVPR, 2011
15. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310. 1531, 2013
16. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv preprint arXiv:1311. 2524, 2013
17. M. Guerini and J. Staiano. Deep feelings: A massive cross-lingual study on the relation between emotions and virality. arXiv preprint arXiv:1503. 04723, 2015
18. M. Guerini, J. Staiano, and D. Albanese. Exploring image virality in google plus. In Social Computing (SocialCom), 2013 International Conference on, pages 671-678. IEEE, 2013
19. P. Isola, D. Parikh, A. Torralba, and A. Oliva. Understanding the intrinsic memorability of images. In NIPS, 2011
20. P. Isola, J. Xiao, A. Torralba, and A. Oliva. What makes an image memorable? In CVPR, 2011
21. E. Johns, O. Mac Aodha, and G. J. Brostow. Becoming the Expert-Interactive Multi-Class Machine Teaching. In CVPR, 2015
22. T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. In Computer Vision, 2009 IEEE 12th international conference on, pages 2106-2113. IEEE, 2009
23. A. Khosla, A. D. Sarma, and R. Hamid. What makes an image popular? In International World Wide Web Conference (WWW), Seoul, Korea, April 2014
24. C. Kiddon and Y. Brun. That's what she said: Double entendre identification. In ACL (Short Papers), 2011
25. H. Lakkaraju, J. McAuley, and J. Leskovec. What's in a name? understanding the interplay between titles, content, and communities in social media. ICWSM, 2013
26. J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. Transactions on the Web, 2007
27. M. Nagarajan, H. Purohit, and A. P. Sheth. A qualitative examination of topical tweet and retweet practices. In ICWSM, 2010
28. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 2001
29. D. Parikh and K. Grauman. Relative attributes. In ICCV, 2011
30. P. Shakarian, S. Eyre, and D. Paulo. A scalable heuristic for viral marketing under the tipping model, 2013
31. M. Spain and P. Perona. Measuring and predicting object importance. IJCV, 2011
32. B. Suh, L. Hong, P. Pirolli, and E. H. Chi. Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In Social Computing, 2010
33. A. Torralba. Contextual priming for object detection. IJCV, 2003
34. A. Torralba and A. A. Efros. Unbiased look at dataset bias. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1521-1528. IEEE, 2011
35. A. Torralba, R. Fergus, and W. T. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. TPAMI, 2008
36. N. Turakhia and D. Parikh. Attribute dominance: What pops out? In ICCV, 2013
37. W. Y. Wang and M. Wen. I can has cheezburger? a nonparanormal approach to combining textual and visual information for predicting and generating popular meme descriptions. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015
38. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010.