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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:589143 |
| Date | January 2012 |
| Creators | Fang, Yan |
| Contributors | Hancock, Edwin |
| Publisher | University of York |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.whiterose.ac.uk/4778/ |
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