Two Step Relevance Feedback for Semantic Disambiguation in Image Retrieval
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
Learning to rank for content-based image retrieval
Proceedings of the international conference on Multimedia information retrieval
Spatio-temporal tube kernel for actor retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Text segmentation in natural scenes using toggle-mapping
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
SALSAS: Sub-linear active learning strategy with approximate k-NN search
Pattern Recognition
Multimedia Tools and Applications
Enhancing image retrieval by an exploration-exploitation approach
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting
Engineering Applications of Artificial Intelligence
Active learning for interactive segmentation with expected confidence change
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Active labeling application applied to food-related object recognition
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
Active learning via neighborhood reconstruction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extension are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.