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

Xingqi WANG, Thorsten SCHINER, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)

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. © 2007 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)2879-2884
Number of pages6
JournalExpert Systems with Applications
Volume34
Issue number4
Early online date19 May 2007
DOIs
Publication statusPublished - May 2008
Externally publishedYes

Bibliographical note

We would like to acknowledge the supports of Scientific Research Fund of Zhejiang Provincial Education Department, China, under Grant No. 20040458 and Hangzhou Dianzi University (HDU), China. We would also like to thank Tim Hounsome and Gavin Wilson from Biocensus, UK for their valuable comments and evaluations.

Keywords

  • Bird identification
  • Entropy
  • Fuzzy similarity

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