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Data Clustering and Graph-Based Image Matching Methods

Fang, Yan (2012) Data Clustering and Graph-Based Image Matching Methods. PhD thesis, University of York.

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Abstract

This thesis describes our novel methods for data clustering, graph characterizing and image matching. In Chapter 3, our main contribution is the M1NN agglomerative clustering method with a new parallel merging algorithm. A cluster characterizing quantity is derived from the path-based dissimilarity measure. In Chapter 4, our main contribution is the modified log likelihood model for quantitative clustering analysis. The energy of a graph is adopted to define the description length to measure the complexity of a clustering. In Chapter 5, our main contribution is an image matching method based on Delaunay graph characterization and node selection. A normalized Euclidean distance on Delaunay graphs is found useful to estimate pairwise distances.

Item Type: Thesis (PhD)
Keywords: data clustering, image matching, graph, computer vision and pattern recognition
Academic Units: The University of York > Computer Science (York)
Identification Number/EthosID: uk.bl.ethos.589143
Depositing User: Mr Yan Fang
Date Deposited: 16 Dec 2013 14:38
Last Modified: 08 Sep 2016 13:29
URI: http://etheses.whiterose.ac.uk/id/eprint/4778

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