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Graph Based Filtering and Matching for Symbol Recognition

Journal of Signal and Information Processing 2018年第3期9卷 167-191页

作者： vaishali pawar Mukesh Zaveri Department of Computer Engineering SVNIT Surat India

Pattern recognition is a task of searching particular patterns or features in the given input. The data mining, computer networks, genetic engineering, chemical structure analysis, web services etc. are few rapidly growing applications where pattern recognition has been used. Graphs are very powerful model applied in various areas of computer science and engineering. This paper proposes a graph based algorithm for performing the graphical symbol recognition. In the proposed approach, a graph based filtering prior to the matching is performed which significantly reduces the computational complexity. The proposed algorithm is evaluated using a large number of input drawings and the simulation results show that the proposed algorithm outperforms the existing algorithms.

关键词： Graphs Pattern Recognition Information Retrieval Graph Matching Data Mining

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K-Means Graph Database Clustering and Matching for Fingerprint Recognition

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Intelligent Information Management 2015年第4期7卷 242-251页

作者： vaishali pawar Mukesh Zaveri Department of Computer Engineering SVNIT Surat India

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.

关键词： Pattern Recognition Fingerprint Matching Graph Matching Clustering

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