Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Experimental result analysis for a generative probabilistic image retrieval model
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Learning Bayesian Networks
Efficient semantic annotation method for indexing large personal video database
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Optimizing multi-graph learning: towards a unified video annotation scheme
Proceedings of the 15th international conference on Multimedia
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We propose a novel algorithm for extracting information by mining the feature space clusters and then assigning salient concepts to them. Bayesian techniques for extracting concepts from multimedia usually suffer either from lack of data or from too complex concepts to be represented by a single statistical model. An incremental information extraction approach, working at different levels of abstraction, would be able to handle concepts of varying complexities. We present the results of our research on the initial part of an incremental approach, the extraction of the most salient concepts from multimedia information.