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Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
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Object Categorization by Learned Universal Visual Dictionary
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The feature and spatial covariant kernel: adding implicit spatial constraints to histogram
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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International Journal of Computer Vision
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Pattern Recognition Letters
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Computer Vision and Image Understanding
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Discovering multipart appearance models from captioned images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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Pattern Recognition Letters
Unsupervised selective transfer learning for object recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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Computer Vision and Image Understanding
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International Journal of Computer Vision
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Learning-based object segmentation using regional spatial templates and visual features
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Learning semantic representations of objects and their parts
Machine Learning
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In this paper we investigate a new method of learning part-based models for visual object recognition, from training data that only provides information about class membership (and not object location or configuration). This method learns both a model of local part appearance and a model of the spatial relations between those parts. In contrast, other work using such a weakly supervised learning paradigm has not considered the problem of simultaneously learning appearance and spatial models. Some of these methods use a “bag” model where only part appearance is considered whereas other methods learn spatial models but only given the output of a particular feature detector. Previous techniques for learning both part appearance and spatial relations have instead used a highly supervised learning process that provides substantial information about object part location. We show that our weakly supervised technique produces better results than these previous highly supervised methods. Moreover, we investigate the degree to which both richer spatial models and richer appearance models are helpful in improving recognition performance. Our results show that while both spatial and appearance information can be useful, the effect on performance depends substantially on the particular object class and on the difficulty of the test dataset.