Cross-domain video concept detection using adaptive svms

ABSTRACT

Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.

REFERENCES

• 1: G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In Proc. of Neural Information Processing Systems.
• 2: S. J. Delany, P. Cunningham, A. Tsymbal, and L. Coyle. A case-based technique for tracking concept drift in spam filtering. Knowledge-Based Systems, 18(4-5):187--195, 2005.
• 3: Dayong Ding , Bo Zhang, Probabilistic model supported rank aggregation for the semantic concept detection in video, Proceedings of the 6th ACM international conference on Image and video retrieval, p.587-594, July 09-11, 2007, Amsterdam, The Netherlands [doi>10.1145/1282280.1282364]
• 4: H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik. Support vector regression machines. pages 155--161, 1996.
• 5: T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning: Data mining, inference, and prediction.
• 6: Ralf Klinkenberg , Thorsten Joachims, Detecting Concept Drift with Support Vector Machines, Proceedings of the Seventeenth International Conference on Machine Learning, p.487-494, June 29-July 02, 2000
• 7: Jeremy Z. Kolter , Marcus A. Maloof, Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift, Proceedings of the Third IEEE International Conference on Data Mining, p.123, November 19-22, 2003
• 8: Xuejun Liao , Ya Xue , Lawrence Carin, Logistic regression with an auxiliary data source, Proceedings of the 22nd international conference on Machine learning, p.505-512, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102415]
• 9: R. Manmatha , T. Rath , F. Feng, Modeling score distributions for combining the outputs of search engines, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.267-275, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384005]
• 10: M. R. Naphade, L. Kennedy, J. R. Kender, S. F.Chang, J. Smith, P. Over, and A. Hauptmann. A lightscale concept ontology for multimedia understanding for TRECVID 2005. In IBM Research Technical Report, 2005.
• 11: Apostol (Paul) Natsev , Milind R. Naphade , Jelena TešiĆ, Learning the semantics of multimedia queries and concepts from a small number of examples, Proceedings of the 13th annual ACM international conference on Multimedia, November 06-11, 2005, Hilton, Singapore [doi>10.1145/1101149.1101288]
• 12: John C. Platt, Fast training of support vector machines using sequential minimal optimization, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
• 13: A. Smeaton and P. Over. Trecvid: Benchmarking the effectiveness of information retrieval tasks on digital video. In Proc. of the Intl. Conf. on Image and Video Retrieval, 2003.
• 14: Cees G. M. Snoek , Marcel Worring , Jan C. van Gemert , Jan-Mark Geusebroek , Arnold W. M. Smeulders, The challenge problem for automated detection of 101 semantic concepts in multimedia, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA [doi>10.1145/1180639.1180727]
• 15: N. Syed, H. Liu, and K. Sung. Incremental learning with support vector machines. In In Proc. of the Workshop on Support Vector Machines, at the Int'l Joint Conf. on Articial Intelligence, 1999.
• 16: Haixun Wang , Wei Fan , Philip S. Yu , Jiawei Han, Mining concept-drifting data streams using ensemble classifiers, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956778]
• 17: Pengcheng Wu , Thomas G. Dietterich, Improving SVM accuracy by training on auxiliary data sources, Proceedings of the twenty-first international conference on Machine learning, p.110, July 04-08, 2004, Banff, Alberta, Canada [doi>10.1145/1015330.1015436]
• 18: Xiaoyun Wu , Rohini Srihari, Incorporating prior knowledge with weighted margin support vector machines, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014089]
• 19: Yi Wu , Edward Y. Chang , Kevin Chen-Chuan Chang , John R. Smith, Optimal multimodal fusion for multimedia data analysis, Proceedings of the 12th annual ACM international conference on Multimedia, October 10-16, 2004, New York, NY, USA [doi>10.1145/1027527.1027665]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Algorithms, Experimentation, Performance

Keywords:
adaptive SVMs, classifier adaptation, cross-domain video concept detection