Hlaoui, A. and Wang, S. (2004) Approximate Graph Matching and Computing Median Graph for Graph Clustering. Proceedings of the International Workshop on Multidisciplinary Image, Video, and Audio Retrieval and Mining, Quebec, 25-26 October 2004.

has been cited by the following article:

• TITLE: K-Means Graph Database Clustering and Matching for Fingerprint Recognition

  AUTHORS: Vaishali Pawar, Mukesh Zaveri

  KEYWORDS: Pattern Recognition, Fingerprint Matching, Graph Matching, Clustering

  JOURNAL NAME: Intelligent Information Management, Vol.7 No.4, July 30, 2015

  ABSTRACT: The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.