PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

High Order Structural Matching Using Dominant Cluster Analysis
Peng Ren, Richard Wilson and Edwin Hancock
In: Image Analysis and Processing - ICIAP 2011 - 16th International Conference, September 14-16, 2011, Ravenna, Italy.

Abstract

We formulate the problem of high order structural matching by applying dominant cluster analysis (DCA) to a direct product hypergraph (DPH). For brevity we refer to the resulting algorithm as DPH-DCA. The DPH-DCA can be considered as an extension of the game theoretic algorithms presented in from clustering to matching, and also as a reduced version of reduced version of the method of ensembles of affinity relations presented in [6]. The starting point for our method is to construct a K-uniform direct product hypergraph for the two sets of higher-order features to be matched. Each vertex in the direct product hypergraph represents a potential correspondence and the weight on each hyperedge represents the agreement between two K-tuples drawn from the two feature sets. Vertices representing correct assignment tend to form a strongly intra-connected cluster, i.e. a dominant cluster. We evaluate the association of each vertex belonging to the dominant cluster by maximizing an objective function which maintains the K-tuple agreements. The potential correspondences with nonzero association weights are more likely to belong to the dominant cluster than the remaining zero-weighted ones. They are thus selected as correct matchings subject to the one-to-one correspondence constraint. Furthermore, we present a route to improving the matching accuracy by invoking prior knowledge. An experimental evaluation shows that our method outperforms the state-of-the-art high order structural matching methods.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:8544
Deposited By:Edwin Hancock
Deposited On:13 February 2012