PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving "bag-of-keypoints" image categorisation: Generative Models and PDF-Kernels
Jason Farquhar, Sandor Szedmak, Hongying Meng and John Shawe-Taylor
PASCAL Eprint Series 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 dened. This approach is shown to give state of the art categorisation performance.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:8157
Deposited By:John Shawe-Taylor
Deposited On:10 June 2011