Learning Sparse Covariance Patterns for Natural Scenes
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Abstract
For scene classification, patch-level linear features do
not always work as well as hand-crafted features. In this
paper, we present a new model to greatly improve the discrimination
power of linear features in classification by introducing
covariance patterns. We analyze the properties of
covariance, along with their fundamental importance, and
present a generative model to properly utilize them. With
this set of covariance information, in our framework, even
the most naive linear features that originally lack the vital
ability in classification become powerful. Experiments
show that the performance of our new covariance model
based on linear features is comparable with or even better
than hand-crafted features in scene classification.
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2012-06
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