Automatic feature-queried bird identification system based on entropy and fuzzy similarity

  • Authors:

  • Xingqi Wang;Thorsten Schiner;Xin Yao

  • Affiliations:

  • CERCIA, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom and School of Computer Science, Hangzhou Dianzi University, Xiasha, Hangzhou 310018, ...;CERCIA, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom;CERCIA, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

Birdwatching is one of the very interesting hobbies and most important work. Many birdwatching assistant systems have been developed. However, most of them do not have any intelligence and cannot tolerate noises either. A bird identification system, BirdID is proposed and implemented. To identify birds, BirdID imitates bird experts to automatically direct birdwatchers to provide features. It also tries to list the most likely species after each feature is entered. In BirdID, entropy and fuzzy similarity are used to select most appropriate queried features and calculate similarity, respectively, which makes BirdID more intelligent and noise-tolerant. The experiments on a dataset with 106 species show that BirdID works well.