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|Title:||Improving "bag-of-keypoints" image categorisation: Generative Models and PDF-Kernels|
|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 . 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  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.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
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