Structured learning approach to image descriptor combination
Structured learning approach to image descriptor combination
- Author(s): J. Zhou ; Z. Fu ; A. Robles-Kelly
- DOI: 10.1049/iet-cvi.2010.0080
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- Author(s): J. Zhou 1, 2 ; Z. Fu 3 ; A. Robles-Kelly 1, 2
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View affiliations
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Affiliations:
1: NICTA, Canberra, Australia
2: College of Engineering and Computer Science, ANU, Canberra, Australia
3: Faculty of Information Technology, Monash University, Australia
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Affiliations:
1: NICTA, Canberra, Australia
- Source:
Volume 5, Issue 2,
March 2011,
p.
134 – 142
DOI: 10.1049/iet-cvi.2010.0080 , Print ISSN 1751-9632, Online ISSN 1751-9640
In this study, the authors address the problem of combining descriptors for purposes of object categorisation and classification. The authors cast the problem in a structured learning setting by viewing the classifier bank and the codewords used in the categorisation and classification tasks as random fields. In this manner, the authors can abstract the problem into a graphical model setting, in which the fusion operation is a transformation over the field of descriptors and classifiers. Thus, the problem reduces itself to that of recovering the optimal transformation using a cost function which is convex and can be converted into either a quadratic or linear programme. This cost function is related to the target function used in discrete Markov random field approaches. The authors demonstrate the utility of our algorithm for purposes of image classification and learning class categories on two datasets.
Inspec keywords: image classification; convex programming; learning (artificial intelligence); Markov processes; image fusion
Other keywords:
Subjects: Computer vision and image processing techniques; Optimisation techniques; Markov processes; Optimisation techniques; Optical, image and video signal processing; Knowledge engineering techniques; Markov processes
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