Yang-Ta Kao This email address is being protected from spambots. You need JavaScript enabled to view it.1, Hwei-Jen Lin2 and Hung-Hsuan Wu2

1Department of Information Network Technology, Chihlee Institute of Technology, Taipei, Taiwan 220, R.O.C.
2Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C.


 

Received: March 3, 2006
Accepted: June 10, 2006
Publication Date: September 1, 2007

Download Citation: ||https://doi.org/10.6180/jase.2007.10.3.09  


ABSTRACT


Shape representation and matching is a not only important but also challenging part in image retrieval. In our previous work, a shape-based image retrieval system was proposed. The Mountain Climbing Sequence (MCS) is used to represent the shape feature of an object, which is invariant to some transformations such as translation, scaling, rotation, and even reversion, and also has a high tolerance for occlusion and noise. In this paper we improve this system, in terms of both retrieval precision and time efficiency, by adopting the Longest Common Subsequence (LCS) matching mechanism to match two MCSs more effectively and utilizing the Selection algorithm to speed up the retrieval process. Experimental results show that the improved system has far better performance. Especially it copes with the occlusion problem very well; that is, it has higher tolerance for occlusion.


Keywords: Transformation-Invariance, Clustering, Mountain Climbing Sequence (MCS), Longest Common Subsequence (LCS), Selection Algorithm


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