Balanced feature matching in probabilistic framework and its application on object localisation
by Ying Chen; Chunlu Ai
International Journal of Computer Applications in Technology (IJCAT), Vol. 46, No. 2, 2013

Abstract: A new algorithm of feature matching is proposed after balancing analysis of adjacency matrix of the matching model in a probabilistic framework. Considering all the interaction of the two feature point sets, a probabilistic model is established and solved using random walks with restart (RWR). To reduce the influence of deformation, and increase the accuracy of feature matching algorithm, a balancing analysis to the adjacency matrix of RWR is taken. Then an efficient method for bidirectional balance is presented, which makes the relevance weight between each two correspondence candidates balanced. The approach considers not only all the correspondence candidates of the two feature point sets, but also the geometrical relation between each pair of candidates. It improves the discriminative and accuracy performance of matching. Compared with other state-of-the-art algorithms, the method is more robust to outliers and geometric deformation, and is accurate in terms of matching rate in various matching applications, such as object localisation.

Online publication date: Wed, 29-May-2013

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