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Unsupervised object class discovery via bottom up multiple class learning
Unsupervised object class discovery via bottom up multiple class learning
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机译:通过自下而上的多类学习进行无监督的对象类发现
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摘要
Techniques for unsupervised object class discovery via bottom-up multiple class learning are described. These techniques may include receiving multiple images containing one or more object classes. The multiple images may be analyzed to extract top saliency instances and least saliency instances. These saliency instances may be clustered to generate and/or update statistical models. The statistical models may be used to discover the one or more object classes. In some instances, the statistical models may be used to discover object classes of novel images.
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