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 concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.
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Panda, N., Goh, KS. & Chang, E.Y. Active learning in very large databases. Multimed Tools Appl 31, 249–267 (2006). https://doi.org/10.1007/s11042-006-0043-1
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DOI: https://doi.org/10.1007/s11042-006-0043-1