Symbiotic segmentation and part localization for fine-grained categorization

Yuning Chai, Victor Lempitsky, Andrew Zisserman

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    196 Citations (Scopus)


    We propose a new method for the task of fine-grained visual categorization. The method builds a model of the base-level category that can be fitted to images, producing high-quality foreground segmentation and mid-level part localizations. The model can be learnt from the typical datasets available for fine-grained categorization, where the only annotation provided is a loose bounding box around the instance (e.g. bird) in each image. Both segmentation and part localizations are then used to encode the image content into a highly-discriminative visual signature. The model is symbiotic in that part discovery/localization is helped by segmentation and, conversely, the segmentation is helped by the detection (e.g. part layout). Our model builds on top of the part-based object category detector of Felzenszwalb et al., and also on the powerful Grab Cut segmentation algorithm of Rother et al., and adds a simple spatial saliency coupling between them. In our evaluation, the model improves the categorization accuracy over the state-of-the-art. It also improves over what can be achieved with an analogous system that runs segmentation and part-localization independently.

    Original languageEnglish
    Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages8
    ISBN (Print)9781479928392
    Publication statusPublished - 2013
    Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
    Duration: 1 Dec 20138 Dec 2013

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision


    Conference2013 14th IEEE International Conference on Computer Vision, ICCV 2013
    CitySydney, NSW


    • Computer Vision
    • Detection
    • Fine-Grained
    • Object Recognition
    • Segmentation


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