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

Xingqi WANG; Thorsten SCHINER; Xin YAO

doi:10.1016/j.eswa.2007.05.045

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

Xingqi WANG, Thorsten SCHINER, Xin YAO

Research output: Journal Publications › Journal 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 language	English
Pages (from-to)	2879-2884
Number of pages	6
Journal	Expert Systems with Applications
Volume	34
Issue number	4
Early online date	19 May 2007
DOIs	https://doi.org/10.1016/j.eswa.2007.05.045
Publication status	Published - May 2008
Externally published	Yes

Bibliographical note

We would also like to thank Tim Hounsome and Gavin Wilson from Biocensus, UK for their valuable comments and evaluations.

Funding

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.

Keywords

Bird identification
Entropy
Fuzzy similarity

Access to Document

10.1016/j.eswa.2007.05.045

Cite this

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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. {\textcopyright} 2007 Elsevier Ltd. All rights reserved.",

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AU - YAO, Xin

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PY - 2008/5

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AB - 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.

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KW - Fuzzy similarity

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

Abstract

Bibliographical note

Funding

Keywords

Access to Document

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