Multimedia Tools and Applications

, Volume 31, Issue 3, pp 249–267 | Cite as

Active learning in very large databases



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.


Active learning Image retrieval Relevance feedback Support vector machines 


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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.University of CaliforniaSanta BarbaraUSA

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