On scalability of active learning for formulating query concepts

Authors:
Wei-Cheng Lai;Kingshy Goh;Edward Y. Chang
Affiliations:
University of California, Santa Barbara, CA;University of California, Santa Barbara, CA;University of California, Santa Barbara, CA
Venue:
Proceedings of the 1st international workshop on Computer vision meets databases
Year:
2004

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Abstract

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 dataset size and in concept complexity. We present remedies, explain limitations, and discuss future directions that research might take.