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IEEE Base papers Details :
Paper Title : Active learning methods for interactive image retrieval
Paper Authors : Philippe Henri Gosselin and Matthieu Cord
IEEE Transactions on : Image Processing
IEEE Base papers Abstract :
Active learning methods have been regarded with increased attention in the mathematical studying group. Initially developed within a category structure, a lot of plug-ins are now being suggested to deal with multimedia programs. This paper provides methods within a mathematical structure to boost active learning for on the internet content-based image retrieval (CBIR).
The classification structure is provided with tests to compare several highly effective category methods in this information retrieval perspective. Concentrating on entertaining methods, active learning strategy is then described. The restrictions of this strategy for CBIR are highlighted before introducing our new active selection process RETIN. First, as any active technique is delicate to the boundary evaluation between sessions, the RETIN technique carries out a edge modification to make the collection procedure more robust. Second, the requirements of generalization mistake to optimize the active studying choice is customized to better signify the CBIR purpose of data source position.
Third, a set handling of images is suggested. Our technique causes a quick and effective active learning program to recover places of on the internet pictures (query concept). Experiments on large directories display that the RETIN method performs well in evaluation to several other active methods.