Abstract
Activity videos are widespread on the Internet but current video search is limited to text tags due to limitations in recognition systems. One of the main reasons for this limitation is the wide variety of activities users could query. Thus codifying knowledge for all queries becomes problematic. Relevance Feedback (RF) is a retrieval framework that addresses this issue via interactive feedback with the user during the search session. An added benefit is that RF can also learn the subjective component of a user’s search preferences. However for good retrieval performance, RF may require a large amount of user feedback for activity search. We address this issue by introducing Transfer Learning (TL) into RF. With TL, we can use auxiliary data from known classification problems different from the user’s target query to decrease the needed amount of user feedback. We address key issues in integrating RF and TL and demonstrate improved performance on the challenging YouTube Action Dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Canu, S., Grandvalet, Y., Guigue, V., Rakotomamonjy, A.: SVM and kernel methods matlab toolbox. Perception Systmes et Information, INSA de Rouen, Rouen, France (2005)
Cao, L., Liu, Z., Huang, T.: Cross dataset action detection. In: CVPR. IEEE, Los Alamitos (2010)
Chen, L., Chin, K., Liao, H.: An integrated approach to video retrieval. In: ADC. Australian Computer Society, Inc. (2008)
Crucianu, M., Ferecatu, M., Boujemaa, N.: Relevance feedback for image retrieval: a short survey. State of the art in audiovisual content-based retrieval, information universal access and interaction including data models and languages, DELOS2 Report (FP6 NoE) (2004)
Duan, L., Xu, D., Tsang, I., Luo, J.: Visual event recognition in videos by learning from web data. In: CVPR. IEEE, Los Alamitos (2010)
Hauptmann, A., Lin, W., Yan, R., Yang, J., Chen, M.: Extreme video retrieval: joint maximization of human and computer performance. In: MULTIMEDIA. ACM, New York (2006)
Hu, Y., Cao, L., Lv, F., Yan, S., Gong, Y., Huang, T.: Action detection in complex scenes with spatial and temporal ambiguities. In: ICCV. IEEE, Los Alamitos (2009)
Laptev, I.: On space-time interest points. International Journal of Computer Vision 64, 107–123 (2005)
Liu, J., Luo, J., Shah, M.: Recognizing realistic actions from videos “in the wild”. In: CVPR. IEEE, Los Alamitos (2009)
Liu, X., Zhuang, Y., Pan, Y.: A new approach to retrieve video by example video clip. In: MULTIMEDIA. ACM, New York (1999)
Liu, Y., Xu, D., Tsang, I., Luo, J.: Using large-scale web data to facilitate textual query based retrieval of consumer photos. In: MULTIMEDIA. ACM, New York (2009)
Luan, H., Zheng, Y., Neo, S., Zhang, Y., Lin, S., Chua, T.: Adaptive multiple feedback strategies for interactive video search. In: CIVR. ACM, New York (2008)
Pan, S., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering (2009)
Rocchio, J.: Relevance Feedback in Information Retrieval, pp. 313–323. Prentice-Hall, Inc., Englewood Cliffs (1971)
Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowledge and Engineering Review 18, 95–145 (2003)
Ryoo, M., Aggarwal, J.: Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In: ICCV. IEEE, Los Alamitos (2009)
Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2010)
Setz, A., Snoek, C.: Can social tagged images aid concept-based video search? In: ICME. IEEE, Los Alamitos (2009)
Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: MULTIMEDIA. ACM, New York (2001)
Yang, J., Yan, R., Hauptmann, A.: Cross-domain video concept detection using adaptive SVMs. In: MULTIMEDIA. ACM, New York (2007)
Yang, J., Hauptmann, A.: A framework for classifier adaptation and its applications in concept detection. In: MIR. ACM, New York (2008)
Yao, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: CVPR. IEEE, Los Alamitos (2010)
Zhou, X., Huang, T.: Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems 8, 536–544 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lam, A., Roy-Chowdhury, A.K., Shelton, C.R. (2011). Interactive Event Search through Transfer Learning. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-19318-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19317-0
Online ISBN: 978-3-642-19318-7
eBook Packages: Computer ScienceComputer Science (R0)