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2003
IEEE

Statistical learning for effective visual information retrieval

11 years 15 hour ago
Statistical learning for effective visual information retrieval
For effective retrieval of visual information, statistical learning plays a pivotal role. Statistical learning in such a context faces at least two major mathematical challenges: scarcity of training data, and imbalance of training classes. We present these challenges and outline our methods for addressing them: active learning, recursive subspace co-training, adaptive dimensionality reduction, class-boundary alignment, and quasi-bagging. 1 Overview The principal design goal of a visual information retrieval system is to return data (images or video clips) that accurately match users' query concepts. To achieve this design goal, the system must first comprehend a user's query concept thoroughly, and then find data that match the concept in the low-level input space accurately. Statistical learning techniques can assist achieving the design goal via two complementary avenues: semantic annotation and query-concept learning. Semantic annotation provides visual data with semanti...
Edward Y. Chang, Beitao Li, Gang Wu, Kingshy Goh
Added 24 Oct 2009
Updated 24 Oct 2009
Type Conference
Year 2003
Where ICIP
Authors Edward Y. Chang, Beitao Li, Gang Wu, Kingshy Goh
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