Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Cross-media manifold learning for image retrieval & annotation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Expert Systems with Applications: An International Journal
Correlative linear neighborhood propagation for video annotation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Joint learning of labels and distance metric
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Improving video concept detection using spatio-temporal correlation
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Semisupervised kernel matrix learning by kernel propagation
IEEE Transactions on Neural Networks
Hypergraph with sampling for image retrieval
Pattern Recognition
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Manifold-ranking based retrieval using k-regular nearest neighbor graph
Pattern Recognition
Information Sciences: an International Journal
Bidirectional-isomorphic manifold learning at image semantic understanding & representation
Multimedia Tools and Applications
Content based image retrieval via a transductive model
Journal of Intelligent Information Systems
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In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR , our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques