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Pattern recognition with local invariant features

Cordelia Schmid 1, * Gyuri Dorkó 1 Svetlana Lazebnik 2 Krystian Mikolajczyk 1 Jean Ponce 2
* Corresponding author
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
2 The Beckman Institute for Advanced Science and Technology
Abstract : Local invariant features have shown to be very successful for recognition. They are robust to occlusion and clutter, distinctive as well as invariant to image transformations. In this chapter recent progress on local invariant features is summarized. It is explained how to extract scale and affine-invariant regions and how to obtain discriminant descriptors for these regions. It is then demonstrated that combining local features with pattern classification techniques allows for texture and category-level object recognition in the presence of varying viewpoints and background clutter.
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https://hal.inria.fr/inria-00548523
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Submitted on : Monday, December 20, 2010 - 9:09:05 AM
Last modification on : Monday, December 28, 2020 - 3:44:02 PM

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Cordelia Schmid, Gyuri Dorkó, Svetlana Lazebnik, Krystian Mikolajczyk, Jean Ponce. Pattern recognition with local invariant features. C.H. Chen and P.S.P Wang. Handbook of Pattern Recognition and Computer Vision, World Scientific, pp.71-92, 2005, 978-981-256-105-3. ⟨inria-00548523⟩

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