Active learning in social context for image classification

VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications

Volumn 2, Issue , 2014, Pages 76-85

  Chatzilari, Elisavet ^a,b   Nikolopoulos, Spiros ^a   Kompatsiaris, Yiannis ^a   Kittler, Josef ^b  

^a INFORMATION TECHNOLOGIES INSTITUTE   (Greece)

^b UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

Active Learning; Image Classification Multimodal Fusion; Large Scale; Selective Sampling; Social Context; User generated Content

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; SOCIAL NETWORKING (ONLINE);

ACTIVE LEARNING; LARGE SCALE; MULTI-MODAL FUSION; SELECTIVE SAMPLING; SOCIAL CONTEXT; USER-GENERATED CONTENT;

IMAGE CLASSIFICATION;

EID: 84906893418     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5220/0004686400760085     Document Type: Conference Paper

References (29)

1. Campbell, C., Cristianini, N., and Smola, A. J. (2000). Query learning with large margin classifiers. In Proceedings of the Seventeenth International Conference on Machine Learning, ICML '00, pages 111-118, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
2. Chatfield, K., Lempitsky, V., Vedaldi, A., and Zisserman, A. (2011). The devil is in the details: an evaluation of recent feature encoding methods. In British Machine Vision Conference.
3. Chatzilari, E., Nikolopoulos, S., Kompatsiaris, Y., and Kittler, J. (2012). Multi-modal region selection approach for training object detectors. In Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, ICMR '12, pages 5:1-5:8, New York, NY, USA. ACM.
4. Cohn, D., Atlas, L., and Ladner, R. (1994). Improving generalization with active learning. Mach. Learn., 15(2):201-221.
5. Fang, M. and Zhu, X. (2012). I don't know the label: Active learning with blind knowledge. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 2238-2241.
6. Freytag, A., Rodner, E., Bodesheim, P., and Denzler, J. (2013). Labeling examples that matter: Relevancebased active learning with gaussian processes. In Weickert, J., Hein, M., and Schiele, B., editors, GCPR, volume 8142 of Lecture Notes in Computer Science, pages 282-291. Springer.
7. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Ndellec, C. and Rouveirol, C., editors, Machine Learning: ECML-98, volume 1398 of Lecture Notes in Computer Science, pages 137-142. Springer Berlin Heidelberg.
8. Li, X., Snoek, C. G. M., Worring, M., Koelma, D. C., and Smeulders, A. W. M. (2013). Bootstrapping visual categorization with relevant negatives. IEEE Transactions on Multimedia, In press.
9. Lin, H.-T., Lin, C.-J., and Weng, R. C. (2007). A note on platt's probabilistic outputs for support vector machines. Machine Learning, 68(3):267-276.
10. Mark J. Huiskes, B. T. and Lew, M. S. (2010). New trends and ideas in visual concept detection: The mir flickr retrieval evaluation initiative. In MIR '10: Proceedings of the 2010 ACM International Conference on Multimedia Information Retrieval, pages 527-536, New York, NY, USA. ACM.
11. Ng, V. and Cardie, C. (2003). Bootstrapping coreference classifiers with multiple machine learning algorithms. In Proceedings of the 2003 conference on Empirical methods in natural language processing, EMNLP '03, pages 113-120.
12. Nowak, S. and Rüger, S. (2010). How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. In Proceedings of the international conference on Multimedia information retrieval, MIR '10, pages 557-566, New York, NY, USA. ACM.
13. Perronnin, F., Sánchez, J., and Mensink, T. (2010). Improving the fisher kernel for large-scale image classification. In Proceedings of the 11th European conference on Computer vision: Part IV, ECCV'10, pages 143-156. Springer-Verlag.
14. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages 61-74. MIT Press.
15. Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., and Moy, L. (2010). Learning from crowds. J. Mach. Learn. Res., 11:1297-1322.
16. Schohn, G. and Cohn, D. (2000). Less is more: Active learning with support vector machines. In Proceedings of the Seventeenth International Conference on Machine Learning, ICML '00, pages 839-846, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
17. Settles, B. (2009). Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.
18. Smucker, M. D., Allan, J., and Carterette, B. (2007). A comparison of statistical significance tests for information retrieval evaluation. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM '07, pages 623-632.
19. Thomee, B. and Popescu, A. (2012). Overview of the clef 2012 flickr photo annotation and retrieval task. in the working notes for the clef 2012 labs and workshop. Rome, Italy.
20. Tong, S. and Chang, E. (2001). Support vector machine active learning for image retrieval. In Proceedings of the ninth ACM international conference on Multimedia, MULTIMEDIA '01, pages 107-118, New York, NY, USA. ACM.
21. Uricchio, T., Ballan, L., Bertini, M., and Del Bimbo, A. (2013). An evaluation of nearest-neighbor methods for tag refinement.
22. Vedaldi, A. and Fulkerson, B. (2008). VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/.
23. Verma, Y. and Jawahar, C. V. (2012). Image annotation using metric learning in semantic neighbourhoods. In Proceedings of the 12th European conference on Computer Vision-Volume Part III, ECCV'12, pages 836-849.
24. Verma, Y. and Jawahar, C. V. (2013). Exploring svm for image annotation in presence of confusing labels. In Proceedings of the 24th British Machine Vision Conference, BMVC'13.
25. Vijayanarasimhan, S. and Grauman, K. (2011). Large-scale live active learning: Training object detectors with crawled data and crowds. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1449-1456.
26. Wang, M. and Hua, X.-S. (2011). Active learning in multimedia annotation and retrieval: A survey. ACM Trans. Intell. Syst. Technol., 2(2):10:1-10:21.
27. Yan, Y., Rosales, R., Fung, G., and Dy, J. (2011). Active learning from crowds. In Getoor, L. and Scheffer, T., editors, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML '11, pages 1161-1168, New York, NY, USA. ACM.
28. Yan, Y., Rosales, R., Fung, G., Schmidt, M., Hermosillo, G., Bogoni, L., Moy, L., and Dy, J. (2010). Modeling annotator expertise: Learning when everybody knows a bit of something.
29. Zhang, L., Ma, J., Cui, C., and Li, P. (2011). Active learning through notes data in flickr: an effortless training data acquisition approach for object localization. In Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR '11, pages 46:1-46:8, New York, NY, USA. ACM.