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Conference papers

Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

Jianguo Zhang 1 Marcin Marszałek 1 Svetlana Lazebnik 2 Cordelia Schmid 1, * 
* 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
Abstract : Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover's Distance and the ÷2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on 4 texture and 5 object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance.
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Jianguo Zhang, Marcin Marszałek, Svetlana Lazebnik, Cordelia Schmid. Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. Conference on Computer Vision and Pattern Recognition Workshop (Beyond Patches workshop, CVPR '06), Jun 2006, Washington, United States. pp.13, ⟨10.1109/CVPRW.2006.121⟩. ⟨inria-00548574⟩



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