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A Fingerprint Feature Extraction Algorithm based on Optimal Decision for Text Copy Detection

초록

영어

Fingerprint feature-based text copy detection can rapidly identify the plagiarism, but suffers from the excessive fingerprint density. To resolve the problem, we propose a fingerprint feature extraction algorithm based on the optimal decision, combined with Winnowing algorithm and optimal decision model, and it can extract fingerprint feature from the hash values in the sliding window. The experimental results demonstrate that our algorithm can reduce the fingerprint density when the windows’ fingerprint feature is too adjacent, and the selected fingerprints can represent the text feature on the premise of the accuracy of the text copy and the algorithm.

목차

Abstract
 1. Introduction
 2. Optimal Decision-based Fingerprint Feature Extraction
  2.1. Optimal Decision Model
  2.2. Algorithm Description
  2.3. Fingerprint Density Analysis
 3. Experimental Results and Analysis
  3.1. Data-sets and Evaluation Criteria
  3.2. Experimental Results and Analysis
 4. Conclusions
 References

저자정보

  • Guohua Wu School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, P.R.China
  • Mengmeng Zhao School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, P.R.China
  • Lin Han School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, P.R.China
  • Sen Li School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, P.R.China

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