Linking visual concept detection with viewer demographics

Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, ICMR 2012

Volumn , Issue , 2012, Pages

  Ulges, Adrian ^a   Koch, Markus ^b   Borth, Damian ^a,b  

^a GERMAN RESEARCH CENTER FOR ARTIFICIAL INTELLIGENCE DFKI   (Germany)

^b University of Koblenz Landau   (Germany)

Author keywords

Concept detection; Demographics estimation; Video analysis

Indexed keywords

CONCEPT DETECTION; CONTENT-BASED; DATA SETS; DEMOGRAPHIC GROUPS; DEMOGRAPHIC INFORMATION; SEMANTIC CONCEPT; STRONG CORRELATION; TARGET GROUP; TEXTUAL DESCRIPTION; USER PROFILE; VIDEO ANALYSIS; VIDEO CONTENTS; VISUAL CONCEPT; WEB VIDEO; YOUTUBE;

ESTIMATION; SEMANTICS; WEBSITES;

POPULATION STATISTICS;

EID: 84864143280     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2324796.2324827     Document Type: Conference Paper

References (30)

1. P. Atrey, M. Hossain, A. El Saddik, and M. Kankanhalli. Multimodal Fusion for Multimedia Analysis: A Survey. Multimedia Systems, 16(6):345-379, 2010.
2. T. Bozios, G. Lekakos, and V. Skoularidou. Advanced Techniques for Personalized Advertising in a Digital TV Environment: The IMedia System. In Proc. of the eBusiness and eWork Conference, 2001.
3. L. C. et al. IBM Research and Columbia University TRECVID-2011 Multimedia Event Detection (MED) System. In Proc. TRECVID Workshop, 2011.
4. M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The Pascal Visual Object Classes (VOC) Challenge. Int. Journal of Computer Vision, 88(2):303-338, 2010.
5. T. Hofmann. Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning, 42:177-196, 2001. (Pubitemid 32872403)
6. Hollis. Ten Years of Learning on How Online Advertising Builds Brands. Advertising Research, 45:255-268, 2005. (Pubitemid 43388740)
7. A. Hrishikesh, G. Toderici, and J. Yagnik. Video2Text: Learning to Annotate Video Content. In Proc. Workshop on Internet Multim. Mining, 2009.
8. J. Hu, H.-J. Zeng, H. Li, C. Niu, and Z. Chen. Demographic Prediction based on User's Browsing Behavior. In Proc. WWW, pages 151-160, 2007. (Pubitemid 47582247)
9. J. Yan et al. How much can Behavioral Targeting help Online Advertising? In Proc. WWW, pages 261-270, 2009.
10. B. J. Jansen and T. Mullen. Sponsored Search: An Overview of the Concept, History, and Technology. IJEB, 6(2):114-131, 2008.
11. S. Kabbur, E.-H. Han, and G. Karypis. Content-Based Methods for Predicting Web-Site Demographic Attributes. In Proc. ICDM, pages 863-868, 2010.
12. L. Kennedy, S.-F. Chang, and I. Kozintsev. To Search or to Label?: Predicting the Performance of Search-based Automatic Image Classifiers. In Workshop Multimedia Information Retrieval, 2006.
13. X. Li, C. Snoek, and M. Worring. Learning Tag Relevance by Neighbor Voting for Social Image Retrieval. In Proc. MIR, pages 180-187, 2008.
14. H.-T. Lin, C.-J. Lin, and R. Weng. A Note on Platt's Probabilistic Outputs for Support Vector Machines. Mach. Learn., 68(3):267-276, 2007. (Pubitemid 47312490)
15. D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis., 60(2):91-110, 2004.
16. T. Mei, X.-S. Hua, and S. Li. Contextual In-Image Advertising. In Proc. ACM Multimedia, pages 439-448, 2008.
17. T. Mei, X.-S. Hua, and S. Li. VideoSense: A Contextual In-video Advertising System. IEEE Trans. Cir. and Sys. for Video Technol., 19:1866-1879, 2009.
18. P. Over, G. Awad, J. Fiscus, B. Antonishek, A. F. Smeaton, W. Kraaij, and G. Quenot. TRECVID 2010 - An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics. In Proc. TRECVID Workshop, 2010.
19. B. Schölkopf and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. 2001.
20. S. H. Sengamedu, N. Sawant, and S. Wadhwa. vADeo: Video Advertising System. In Proc. ACM Multimedia, pages 455-456, 2007.
21. D. Silversmith. Google Losing up to $ 1.65M a Day on YouTube. available from internetevolution.com (retrieved: December 2011).
22. A. Smeaton. Large Scale Evaluations of Multimedia Information Retrieval: The TRECVid Experience. In Proc. CIVR, pages 11-17, 2005. (Pubitemid 41425538)
23. C. Snoek and M. Worring. Concept-based Video Retrieval. Foundations and Trends in Information Retrieval, 4(2):215-322, 2009.
24. A. Ulges, D. Borth, and T. Breuel. Visual Concept Learning from Weakly Labeled Web Videos. In Video Search and Mining. Springer-Verlag, 2010.
25. A. Ulges, M. Koch, D. Borth, and T. Breuel. TubeTagger - YouTube-based Concept Detection. In Proc. Workshop on Internet Multim. Mining, 2009.
26. X.-J. Wang, M. Yu, L. Zhang, R. Cai, and W.-Y. Ma. Argo: Intelligent Advertising by Mining a User's Interest from his Photo Collections. In Proc. KDD Workshop on Data Mining and Audience Intelligence for Advertising, pages 18-26, 2009.
27. J. Yang and A. Hauptmann. (Un)Reliability of Video Concept Detection. In Proc. Int. Conf. Image and Video Retrieval, pages 85-94, 2008.
28. W.-t. Yih, J. Goodman, and V. R. Carvalho. Finding Advertising Keywords on Web Pages. In Proc. WWW, pages 213-222, 2006. (Pubitemid 46946612)
29. YouTube Press Statistics. available from youtube.com/t/press statistics (retrieved: Mar'12).
30. L. Zelnik-Manor, S. Zanetti, and P. Perona. A Walk Through the Web's Video Clips. In Proc. First Internet Vision Workshop, 2008.