Active learning in very large databases
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Query-by-example and query-by-keyword both suffer from the problem of “aliasing,” meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.
KeywordsActive learning Image retrieval Relevance feedback Support vector machines
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- 1.Blum A, Mitchell T (1998) Combining labeled and unlabeled data wih co-training. In: Proceedings of the workshop on computational learning theory, Madison, Wisconsin, 92–100Google Scholar
- 2.Brinker K (2003) Incorporating diversity in active learning with support vector machines. In: Prooceedings of the twentieth international conference on machine learning, Washington, District of Columbia, 59–66Google Scholar
- 5.Chang E, Li B, Wu G, Goh K-S (2003b) Statistical learning for effective visual information retrieval. In: IEEE Conference in Image Processing, Barcelona, Spain, 606–612Google Scholar
- 6.Flickner M, Sawhney H, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P (1995) Query by image and video content: the QBIC system. IEEE Computer 28(9):23–32Google Scholar
- 7.Goh K, Chang EY, Lai W-C (2004) Concept-dependent multimodal active learning for image retrieval. In: ACM international conference on multimedia, New York, New York, 564–571Google Scholar
- 10.Panda N, Chang E (2005) Exploiting geometry for support vector machine indexing. In: SIAM conference on data mining, Newport Beach, CaliforniaGoogle Scholar
- 11.Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of ACM international conference on multimedia, Ottawa, Canada, 107–118Google Scholar
- 12.Tong S, Koller D (2000) Support vector machine active learning with applications to text classification. In: Proceedings of the 17th international conference on machine learning, Stanford, USA, 401–412Google Scholar
- 14.Zhang Z, Wu G, Wang G, Chang E (2005) Bayesian kernel regression. In: International conference on machine learning, Bonn, GermanyGoogle Scholar