Beyond bags of features: spatial pyramid matching for recognizing natural scene categories

Svetlana Lazebnik 1 Cordelia Schmid 2, * Jean Ponce 3
* Corresponding author
1 Beckman Institute - The Beckman Institute for Advanced Science and Technology
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
3 ENS Paris - École normale supérieure - Paris
Abstract : This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba's "gist" and Lowe's SIFT descriptors.
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Svetlana Lazebnik, Cordelia Schmid, Jean Ponce. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Conference on Computer Vision & Pattern Recognition (CPRV '06), Jun 2006, New York, United States. pp.2169 - 2178, ⟨10.1109/CVPR.2006.68⟩. ⟨inria-00548585⟩

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