Object Categorization

    Research output: Contribution to journalArticle

    Abstract

    This article presents foundations, original research and trends in the
    field of object categorization by computer vision methods. The research
    goals in object categorization are to detect objects in images and to
    determine the object’s categories. Categorization aims for the recognition
    of generic classes of objects, and thus has also been termed
    ‘generic object recognition’. This is in contrast to the recognition of
    specific, individual objects. While humans are usually better in generic
    than in specific recognition, categorization is much harder to achieve
    for today’s computer architectures and algorithms. Major problems are
    related to the concept of a ‘visual category’, where a successful recognition
    algorithm has to manage large intra-class variabilities versus
    sometimes marginal inter-class differences. It turns out that several
    techniques which are useful for specific recognition can also be adapted
    to categorization, but there are also a number of recent developments
    in learning, representation and detection that are especially tailored to
    categorization.
    Recent results have established various categorization methods that
    are based on local salient structures in the images. Some of these methods
    use just a ‘bag of keypoints’ model. Others include a certain amount
    of geometric modeling of 2D spatial relations between parts, or ‘constellations’
    of parts. There is now a certain maturity in these approaches
    and they achieve excellent recognition results on rather complex image
    databases. Further work focused on the description of shape and object
    contour for categorization is only just emerging. However, there remain
    a number of important open questions, which also define current and
    future research directions. These issues include localization abilities,
    required supervision, the handling of many categories, online and incremental
    learning, and the use of a ‘visual alphabet’, to name a few. These
    aspects are illustrated by the discussion of several current approaches,
    including our own patch-based system and our boundary fragmentmodel.
    The article closes with a summary and a discussion of promising
    future research directions.
    Original languageEnglish
    Pages (from-to)255-353
    JournalFoundations and trends in computer graphics and vision
    Volume1
    Issue number4
    DOIs
    Publication statusPublished - 2006

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