Local features and kernels for classification of texture and object categories: A comprehensive study

J. Zhang, M. Marszałek, S. Lazebnik, C. Schmid

Research output: Contribution to journalArticlepeer-review


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 χ2 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 four texture and five 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 via extensive tests on the PASCAL database, for which ground-truth object localization information is available. Our experiments demonstrate that image representations based on distributions of local features are surprisingly effective for classification of texture and object images under challenging real-world conditions, including significant intra-class variations and substantial background clutter.

Original languageEnglish (US)
Pages (from-to)213-238
Number of pages26
JournalInternational Journal of Computer Vision
Issue number2
StatePublished - Jun 2007


  • Image classification
  • Kernel methods
  • Object recognition
  • Scale- and affine-invariant keypoints
  • Support vector machines
  • Texture recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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