Comparison of photo‐matching algorithms commonly used for photographic capture–recapture studies

Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost‐effectiveness. Recently, several computer‐aided photo‐matching algorithms have been developed to more efficiently match images of unique individuals...

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Bibliographic Details
Published in:Ecology and evolution Vol. 7; no. 15; pp. 5861 - 5872
Main Authors: Matthé, Maximilian, Sannolo, Marco, Winiarski, Kristopher, Spitzen ‐ van der Sluijs, Annemarieke, Goedbloed, Daniel, Steinfartz, Sebastian, Stachow, Ulrich
Format: Journal Article
Language:English
Published: England John Wiley & Sons, Inc 01.08.2017
John Wiley and Sons Inc
Subjects:
Algorithms
AmphIdent
APHIS
Capture-recapture studies
capture–recapture
Demographics
Eye
I3S
Image quality
Matching
Object recognition
Original Research
photographic identification
Population number
Wildlife
Wild‐ID
I3S
AmphIdent
APHIS
Wild‐ID
photographic identification
capture–recapture
ISSN:2045-7758, 2045-7758
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Summary:Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost‐effectiveness. Recently, several computer‐aided photo‐matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state‐of‐the‐art photo‐matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo‐matching algorithms; Wild‐ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel‐based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match “by eye” can be easily translated to accurate individual capture histories necessary for robust demographic estimates. The manuscript addresses the question of accuracy of different photo‐matching algorithms for individual identification of wildlife, namely Wild‐ID, I3S Pattern+, APHIS, and AmphIdent. We compare the recognition rate of the four softwares in five different amphibian image databases, which contain between 2,197 and 12,488 images. We found that most softwares yielded an unacceptably low identification rate for the investigated databases, which emphasizes the importance of carefully choosing and checking a matching algorithm before using it for capture–mark–recapture studies.
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ISSN:2045-7758
2045-7758
DOI:10.1002/ece3.3140