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Machine Learning Applications in Computer Vision

Machine Learning Applications in Computer Vision

Mehrtash Harandi, Javid Taheri, Brian C. Lovell
Copyright: © 2013 |Pages: 31
ISBN13: 9781466639942|ISBN10: 1466639946|EISBN13: 9781466639959
DOI: 10.4018/978-1-4666-3994-2.ch045
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MLA

Harandi, Mehrtash, et al. "Machine Learning Applications in Computer Vision." Image Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 896-926. https://doi.org/10.4018/978-1-4666-3994-2.ch045

APA

Harandi, M., Taheri, J., & Lovell, B. C. (2013). Machine Learning Applications in Computer Vision. In I. Management Association (Ed.), Image Processing: Concepts, Methodologies, Tools, and Applications (pp. 896-926). IGI Global. https://doi.org/10.4018/978-1-4666-3994-2.ch045

Chicago

Harandi, Mehrtash, Javid Taheri, and Brian C. Lovell. "Machine Learning Applications in Computer Vision." In Image Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 896-926. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3994-2.ch045

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Abstract

Recognizing objects based on their appearance (visual recognition) is one of the most significant abilities of many living creatures. In this study, recent advances in the area of automated object recognition are reviewed; the authors specifically look into several learning frameworks to discuss how they can be utilized in solving object recognition paradigms. This includes reinforcement learning, a biologically-inspired machine learning technique to solve sequential decision problems and transductive learning, and a framework where the learner observes query data and potentially exploits its structure for classification. The authors also discuss local and global appearance models for object recognition, as well as how similarities between objects can be learnt and evaluated.

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