This work tackles the problem of indirect immunofluorescence images classification. In particular, a dense local descriptor invariant both to scale changes and to rotations is proposed to classify six classes of staining patterns of the HEp-2 cells. In order to provide a compact and discriminative representation, the descriptor combines a log-polar sampling with spatially-varying gaussian smoothing applied on the gradients images in specific directions. Bag-of-Words is finally used to perform classification. Experimental results on the dataset provided in the recent contest hold in 2014 at ICPR show very good performance.

Cell image classification by a scale and rotation invariant dense local descriptor

GRAGNANIELLO, DIEGO;SANSONE, CARLO;
2016

Abstract

This work tackles the problem of indirect immunofluorescence images classification. In particular, a dense local descriptor invariant both to scale changes and to rotations is proposed to classify six classes of staining patterns of the HEp-2 cells. In order to provide a compact and discriminative representation, the descriptor combines a log-polar sampling with spatially-varying gaussian smoothing applied on the gradients images in specific directions. Bag-of-Words is finally used to perform classification. Experimental results on the dataset provided in the recent contest hold in 2014 at ICPR show very good performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4776949
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