BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
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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
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Proceedings of the 12th annual ACM international conference on Multimedia
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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IEEE Transactions on Circuits and Systems for Video Technology
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Automatic image annotation (AIA) has proved to be an effective and promising solution to automatically deduce the high-level semantics from low-level visual features. In this paper, we formulate the task of image annotation as a multi-label, multi class semantic image classification problem and propose a simple yet effective joint classification framework in which probabilistic multi-label boosting and contextual semantic constraints are integrated seamlessly. We conducted experiments on a medium-sized image collection including about 5000 images from Corel Stock Photo CDs. The experimental results demonstrated that the annotation performance of our proposed method is comparable to state-of-the-art approaches, showing the effectiveness and feasibility of the proposed unified framework.