A comparison of active classification methods for content-based image retrieval
Proceedings of the 1st international workshop on Computer vision meets databases
Feature-based approach to semi-supervised similarity learning
Pattern Recognition
Optimization on active learning strategy for object category retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Combining semantic and content based image retrieval in ORDBMS
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Efficiency analysis in content based image retrieval using RDF annotations
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
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This demonstration presents a digital media retrieval system for searching large categories in different media databases. The core of our system is an interactive online classification based on user labeling. The classification is obtained with a statistical learning method: kernels for similarity representation and SVM (Support Vector Machine) using binary user annotations. RETIN applies also an active learning strategy for proposing documents to the user for labeling. The system can deal with images, 3D objects and videos and other media can be added to. A graphical user interface allows easy browsing of different media, simple and user-friendly interaction and fast retrieval.