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Covariance of Covariance Features for Image Classification
Ist Teil von
Proceedings of International Conference on Multimedia Retrieval, 2014, p.411-411
Ort / Verlag
New York, NY, USA: ACM
Erscheinungsjahr
2014
Link zum Volltext
Quelle
ACM Digital Library
Beschreibungen/Notizen
In this paper we propose a novel image descriptor built by computing the covariance of pixel level features on densely sampled patches and encoding them using their covariance. Appropriate projections to the Euclidean space and feature normalizations are employed in order to provide a strong descriptor usable with linear classifiers. In order to remove border effects, we further enhance the Spatial Pyramid representation with bilinear interpolation. Experimental results conducted on two common datasets for object and texture classification show that the performance of our method is comparable with state of the art techniques, but removing any dataset specific dependency in the feature encoding step.