Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9945
Title: Improving "bag-of-keypoints" image categorisation: Generative Models and PDF-Kernels
Authors: Farquhar, J
Szedmak, S
Meng, H
Shawe-Taylor, J
Keywords: Image categorisation;''bag-of-keypoints";GMM;SVM
Issue Date: 2005
Citation: Image Speech and Intelligent Systems, Department of Electronics and Computer Science, 2005
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.
URI: http://bura.brunel.ac.uk/handle/2438/9945
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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