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Symmetry 2017, 9(2), 25; doi:10.3390/sym9020025

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

Department of Electronic Engineering, Inha University, Incheon 22212, Korea
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Received: 7 November 2016 / Revised: 5 January 2017 / Accepted: 4 February 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Symmetry in Complex Networks II)
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Abstract

Deformable objects have changeable shapes and they require a different method of matching algorithm compared to rigid objects. This paper proposes a fast and robust deformable object matching algorithm. First, robust feature points are selected using a statistical characteristic to obtain the feature points with the extraction method. Next, matching pairs are composed by the feature point matching of two images using the matching method. Rapid clustering is performed using the BST (Binary Search Tree) method by obtaining the geometric similarity between the matching pairs. Finally, the matching of the two images is determined after verifying the suitability of the composed cluster. An experiment with five different image sets with deformable objects confirmed the superior robustness and independence of the proposed algorithm while demonstrating up to 60 times faster matching speed compared to the conventional deformable object matching algorithms. View Full-Text
Keywords: content‐based image retrieval; image matching; deformable object; clustering content‐based image retrieval; image matching; deformable object; clustering
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Jeong, J.; Won, I.; Yang, H.; Lee, B.; Jeong, D. Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering. Symmetry 2017, 9, 25.

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