Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Emergent Semantics through Interaction in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
RETIN: a smart interactive digital media retrieval system
Proceedings of the 6th ACM international conference on Image and video retrieval
Computers in Biology and Medicine
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
Object recognition using proportion-based prior information: Application to fisheries acoustics
Pattern Recognition Letters
Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron
Fundamenta Informaticae
Boosted kernel for image categorization
Multimedia Tools and Applications
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For the management of digital document collections, automatic database analysis still has difficulties to deal with semantic queries and abstract concepts that users are looking for. Whenever interactive learning strategies may improve the results of the search, system performances still depend on the representation of the document collection. We introduce in this paper a weakly supervised optimization of a feature vector set. According to an incomplete set of partial labels, the method improves the representation of the collection, even if the size, the number, and the structure of the concepts are unknown. Experiments have been carried out on synthetic and real data in order to validate our approach.