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http://bura.brunel.ac.uk/handle/2438/9945
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DC Field | Value | Language |
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dc.contributor.author | Farquhar, J | - |
dc.contributor.author | Szedmak, S | - |
dc.contributor.author | Meng, H | - |
dc.contributor.author | Shawe-Taylor, J | - |
dc.date.accessioned | 2015-01-27T10:30:03Z | - |
dc.date.available | 2005 | - |
dc.date.available | 2015-01-27T10:30:03Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Image Speech and Intelligent Systems, Department of Electronics and Computer Science, 2005 | en_US |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/9945 | - |
dc.description.abstract | In this paper we propose two distinct enhancements to the basic ''bag-of-keypoints" image categorisation scheme proposed in [4]. In this approach images are represented as a variable sized set of local image features (keypoints). Thus, we require machine learning tools which can operate on sets of vectors. In [4] this is achieved by representing the set as a histogram over bins found by k-means. We show how this approach can be improved and generalised using Gaussian Mixture Models (GMMs). Alternatively, the set of keypoints can be represented directly as a probability density function, over which a kernel can be de ned. This approach is shown to give state of the art categorisation performance. | en_US |
dc.language.iso | en | en_US |
dc.subject | Image categorisation | en_US |
dc.subject | ''bag-of-keypoints" | en_US |
dc.subject | GMM | en_US |
dc.subject | SVM | en_US |
dc.title | Improving "bag-of-keypoints" image categorisation: Generative Models and PDF-Kernels | en_US |
dc.type | Article | en_US |
pubs.confidential | false | - |
pubs.publication-status | Published | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Electronic and Computer Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Electronic and Computer Engineering/Electronic and Computer Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies/Biomedical Engineering and Healthcare Technologies | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
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Fulltext.pdf | 262.71 kB | Adobe PDF | View/Open |
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