Please use this identifier to cite or link to this item:
http://bura.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 Electrical Engineering Research Papers |
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