The nature of statistical learning theory
The nature of statistical learning theory
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Computing conditional probabilities in large domains by maximizing renyi's quadratic entropy
Computing conditional probabilities in large domains by maximizing renyi's quadratic entropy
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
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The integration of content and context information within an image annotation framework is studied, which refer to the low-level visual features and the co-occurrence of different real world objects in a probabilistic sense, respectively. Conventional annotation approaches fail to collect and utilize the context information. Therefore, we proposed a new framework, termed as Collaborative Bayesian Image Annotation (CBIA) framework. 1) In addition to the content information, the proposed system accumulates past annotation results and/or information actively provided by domain experts, from which the context knowledge is extracted. Hence, part of the system is collaboratively constructed by human users. 2) The above information is utilized through a Bayesian framework. Numerical results based on images collected from the Internet demonstrated better performance resulting from the introduction of context knowledge and information fusion.