A system that learns to tag videos by watching youtube

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

Volumn 5008 LNCS, Issue , 2008, Pages 415-424

  Ulges, Adrian ^a,b   Schulze, Christian ^b   Keysers, Daniel ^b   Breuel, Thomas M ^a,b  

^a Rheinland Pfälzische Technische Universität Kaiserslautern Landau

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

Author keywords

Automatic video annotation; Content based video retrieval; Online videos

Indexed keywords

AUTOMATIC VIDEO ANNOTATION; ONLINE VIDEOS;

DATA ACQUISITION; ONLINE SYSTEMS; SEMANTICS; USER INTERFACES;

VIDEO SIGNAL PROCESSING;

EID: 44649182734     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-79547-6_40     Document Type: Conference Paper

References (19)

1. Deselaers, T., Keysers, D., Ney, H.: Discriminative Training for Object Recognition Using Image Patches. In: CVPR, Washington, DC, pp. 157-162 (2005)
2. Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning Object Categories from Google's Image Search. Computer Vision 2, 1816-1823 (2005)
3. Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Bleib, D., Jordan, M.: Matching Words and Pictures. J. Mach. Learn. Res. 3, 1107-1135 (2003)
4. Hoad, T.C., Zobel, J.: Detection of Video Sequences using Compact Signatures. ACM Trans. Inf. Systems 24(1), 1-50 (2006)
5. Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226-239 (1998)
6. Li, J., Wang, J.: Real-time Computerized Annotation of Pictures. In: Intern. Conf. on Multimedia, Santa Barbara, CA, pp. 911-920 (2006)
7. Lienhart, R.: Reliable Transition Detection in Videos: A Survey and Practitioner's Guide. Int. J. of Image and Graphics 1(3), 286-469 (2001)
8. Ma, Y.-F., Zhang, H.-J.: Motion Pattern-based Video Classification and Retrieval. EURASIP J. Appl. Signal Proc. 1, 199-208 (2003)
9. Manjunath, B.S., Ohm, J.-R., Vasudevan, V.V., Yamada, A.: Color and Texture Descriptors. IEEE Trans. on Circuits Syst. for Video Techn. 11(6), 703-715 (2001)
10. Feng, S.L., Manmatha, R., Lavrenko, V.: Multiple Bernoulli Relevance Models for Image and Video Annotation. In: CVPR, Washington, DC, pp. 1002-1009 (2004)
11. Hammoud, R., Mohr, R.: A Probabilistic Framework of Selecting Effective Key Frames for Video Browsing and Indexing. In: Intern. Worksh. on Real-Time Img. Seq. Anal, Oulu, Finland, pp. 79-88 (2000)
12. Naphade, M., Smith, J., Tesic, J., Chang, S.-F., Hsu, W., Kennedy, L., Hauptmann, A., Curtis, J.: Large-Scale Concept Ontology for Multimedia. IEEE Multimedia 3(13), 86-91 (2006)
13. Schwarz, G.: Estimating the Dimension of a Model. Ann. of Stat. 6(2), 461-464 (2003)
14. Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: ICCV, Washington, DC, pp. 1470-1477 (2003)
15. Snoek, C., et al: The MediaMill TRECVID 2006 Semantic Video Search Engine. TRECVID Workshop, Gaithersburg, MD (2006) (unreviewed workshop paper)
16. Tamura, H., Mori, S., Yamawaki, T.: Textural Features Corresponding to Visual Perception. IEEE Trans. on Sys., Man, Cybern. 8(6), 460-472 (1978)
17. Ulges, A., Schulze, C., Keysers, D., Breuel, T.: Content-based Video Tagging for Online Video Portals. In: MUSCLE/ImageCLEF Workshop, Budapest (2007)
18. Snoek, C., Worring, M., van Gemert, J., Geusebroek, J.-M., Smeulders, A.: The Challenge Problem for Automated Detection of 101 Semantic Concepts in Multimedia. In: Intern. Conf. on Multimedia, Santa Barbara, CA, pp. 421-430 (2006)
19. Yang, C., Lozano-Perez, T.: Image Database Retrieval with Multiple-Instance Learning Techniques. In: Int. Conf. on Data Eng., San Diego, CA, pp. 233-243 (2000)