Active learning in very large databases
- 78 Downloads
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
Unable to display preview. Download preview PDF.
- 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