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Neural Computation
Neural Network-Based Face Detection
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Multiple-Instance Learning for Natural Scene Classification
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Configuration based scene classification and image indexing
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Image Indexing Using Color Correlograms
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
JACOB: just a content-based query system for video databases
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Learning similarity measure for natural image retrieval with relevance feedback
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Boosting contextual information in content-based image retrieval
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Fast Recognition of Musical Genres Using RBF Networks
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Kernel-based distance metric learning for content-based image retrieval
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Negative Samples Analysis in Relevance Feedback
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Semantics reinforcement and fusion learning for multimedia streams
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Semi-automatic dynamic auxiliary-tag-aided image annotation
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Biased discriminant euclidean embedding for content-based image retrieval
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We present an approach for image retrieval using a very large number of highly selective features and efficient learning of queries. Our approach is predicated on the assumption that each image is generated by a sparse set of visual “causes” and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 46,000 highly selective features). At query time a user selects a few example images, and the AdaBoost algorithm is used to learn a classification function which depends on a small number of the most appropriate features. This yields a highly efficient classification function. In addition we show that the AdaBoost framework provides a natural mechanism for the incorporation of relevance feedback. Finally we show results on a wide variety of image queries.