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

Jianguo Zhang, Marcin Marszalek, Svetlana Lazebnik, Cordelia Schmid

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    118 Citations (Scopus)

    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.
    Original languageEnglish
    Title of host publicationConference on Computer Vision and Pattern Recognition Workshop, 2006
    Subtitle of host publication(CVPRW'06)
    EditorsCordelia Schmid, Stefano Soatto, Carlo Tomasi
    PublisherIEEE
    Pages93-100
    Number of pages8
    ISBN (Print)0769526462
    DOIs
    Publication statusPublished - 2006
    EventIEEE Computer Society, 2006 Conference on Computer Vision and Pattern Recognition (CVPRW'06) - New York, United States
    Duration: 17 Jun 200622 Jun 2006

    Conference

    ConferenceIEEE Computer Society, 2006 Conference on Computer Vision and Pattern Recognition (CVPRW'06)
    CountryUnited States
    CityNew York
    Period17/06/0622/06/06

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  • Cite this

    Zhang, J., Marszalek, M., Lazebnik, S., & Schmid, C. (2006). Local features and kernels for classification of texture and object categories: a comprehensive study. In C. Schmid, S. Soatto, & C. Tomasi (Eds.), Conference on Computer Vision and Pattern Recognition Workshop, 2006 : (CVPRW'06) (pp. 93-100). IEEE. https://doi.org/10.1109/CVPRW.2006.121