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Object Categorization
Author(s): Axel Pinz
Source:
Journal:Foundations and Trends® in Computer Graphics and Vision
ISSN Print:1572-2740, ISSN Online:1572-2759
Publisher:Now Publishers
Volume 1 Number 4,
Document Type: Article
Pages: 99 (255-353)
DOI: 10.1561/0600000003
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.
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