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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Proceedings of the 23rd international conference on World wide web
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The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. The desired patches need to satisfy two requirements: 1) to be representative, they need to occur frequently enough in the visual world; 2) to be discriminative, they need to be different enough from the rest of the visual world. The patches could correspond to parts, objects, "visual phrases", etc. but are not restricted to be any one of them. We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches. We use an iterative procedure which alternates between clustering and training discriminative classifiers, while applying careful cross-validation at each step to prevent overfitting. The paper experimentally demonstrates the effectiveness of discriminative patches as an unsupervised mid-level visual representation, suggesting that it could be used in place of visual words for many tasks. Furthermore, discriminative patches can also be used in a supervised regime, such as scene classification, where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset.