Journal of the ACM (JACM)
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
PBIR - perception-based image retrieval
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Mining Image Features for Efficient Query Processing
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
An active feedback framework for image retrieval
Pattern Recognition Letters
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Traditional database systems assume that precise query concepts can be specified by users (for example, by using query languages). For many search tasks, however, a query concept is hard to articulate, and articulation can be subjective. Most users would find it hard to describe an image or a music query in low-level perceptual features. We believe that one desirable paradigm for search engines is to mine (i.e., to learn) users' query concepts through active learning. In this paper, we formulate the query-concept learning problem as finding a binary classifier that separates relevant objects from those that are irrelevant to the query concept. We propose two hybrid algorithms, pipeline learning, and co-training, that are built on top of two active learning algorithms. Our empirical study shows that even when the feature dimension is very high and target concepts are very specific, the hybrid algorithms can grasp a complex query concept in a small number of user iterations.