SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
PBIR-MM: multimodal image retrieval and annotation
Proceedings of the tenth ACM international conference on Multimedia
Hybrid Learning Schemes for Multimedia Information Retrieval
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
MEGA---the maximizing expected generalization algorithm for learning complex query concepts
ACM Transactions on Information Systems (TOIS)
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The number of feature required to depict an image can be very large. Using all features simultaneously to measure image similarity and to learn image query-concepts can suffer from the problem of dimensionality curse ,which degrades both search accuracy and search peed. Regarding search accuracy, the presence of irrelevant features with respect to a query can contaminate similarity measurement, and hence decrease both the recall and precision of thatquery. To remedy this problem, we present a mining method that learns online user query concept and identities important features quickly. Regarding search speed, the presence of a large number of feature can low down query-concept learning and indexing performance. We propose a divide-and-conquer method that divides the concept-learning task into G subtasks to achieve speedup. We notice that a task must be divided carefully, or search accuracy maysuffer. We thus propose a genetic-based mining algorithm to discover good feature groupings. Through analysis and mining result, we observe that organizing image features in a multi-resolution manner, and minimizing intra-group feature correlation, can peed up query-concept learning substantially while maintaining high search accuracy.