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    Hierarchical cluster guided labeling: efficient label collection for visual classification

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    http://hdl.handle.net/10217/170415
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    Wigness_colostate_0053A_13392.pdf (15.80Mb)
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    Abstract
    Visual classification is a core component in many visually intelligent systems. For example, recognition of objects and terrains provides perception during path planning and navigation tasks performed by autonomous agents. Supervised visual classifiers are typically trained with large sets of images to yield high classification performance. Although the collection of raw training data is easy, the required human effort to assign labels to this data is time consuming. This is particularly problematic in real-world applications with limited labeling time and resources. Techniques have emerged ...
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    Author(s)
    Wigness, Maggie

    Advisor(s)
    Draper, Bruce

    Date Issued
    2015
    Format
    born digital; doctoral dissertations
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    • 2000-2019 - CSU Theses and Dissertations
    • Theses and Dissertations - Department of Computer Science

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