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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| Vydáno v: | Ecology and evolution Ročník 7; číslo 15; s. 5861 - 5872 |
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John Wiley & Sons, Inc
01.08.2017
John Wiley and Sons Inc |
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| ISSN: | 2045-7758, 2045-7758 |
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| Abstract | 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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| AbstractList | 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. 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. 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.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. 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. |
| Author | Steinfartz, Sebastian Matthé, Maximilian Goedbloed, Daniel Spitzen ‐ van der Sluijs, Annemarieke Winiarski, Kristopher Sannolo, Marco Stachow, Ulrich |
| AuthorAffiliation | 6 Leibniz Centre for Agricultural Landscape Research ZALF Müncheberg Germany 4 Reptile, Amphibian and Fish Conservation the Netherlands Nijmegen the Netherlands 5 Department of Evolutionary Biology Zoological Institute Technische Universität Braunschweig Braunschweig Germany 1 Vodafone Chair Mobile Communication Systems Technical University Dresden Dresden Germany 3 Department of Environmental Conservation University of Massachusetts Amherst MA USA 2 CIBIO, Research Centre in Biodiversity and Genetic Resources InBIO Universidade do Porto Campus de Vairão Vila do Conde Portugal |
| AuthorAffiliation_xml | – name: 6 Leibniz Centre for Agricultural Landscape Research ZALF Müncheberg Germany – name: 5 Department of Evolutionary Biology Zoological Institute Technische Universität Braunschweig Braunschweig Germany – name: 1 Vodafone Chair Mobile Communication Systems Technical University Dresden Dresden Germany – name: 2 CIBIO, Research Centre in Biodiversity and Genetic Resources InBIO Universidade do Porto Campus de Vairão Vila do Conde Portugal – name: 3 Department of Environmental Conservation University of Massachusetts Amherst MA USA – name: 4 Reptile, Amphibian and Fish Conservation the Netherlands Nijmegen the Netherlands |
| Author_xml | – sequence: 1 givenname: Maximilian surname: Matthé fullname: Matthé, Maximilian email: maxi.matthe@googlemail.com organization: Technical University Dresden – sequence: 2 givenname: Marco surname: Sannolo fullname: Sannolo, Marco organization: Campus de Vairão – sequence: 3 givenname: Kristopher surname: Winiarski fullname: Winiarski, Kristopher organization: University of Massachusetts – sequence: 4 givenname: Annemarieke surname: Spitzen ‐ van der Sluijs fullname: Spitzen ‐ van der Sluijs, Annemarieke organization: Reptile, Amphibian and Fish Conservation the Netherlands – sequence: 5 givenname: Daniel surname: Goedbloed fullname: Goedbloed, Daniel organization: Technische Universität Braunschweig – sequence: 6 givenname: Sebastian surname: Steinfartz fullname: Steinfartz, Sebastian organization: Technische Universität Braunschweig – sequence: 7 givenname: Ulrich surname: Stachow fullname: Stachow, Ulrich organization: Leibniz Centre for Agricultural Landscape Research ZALF |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28811886$$D View this record in MEDLINE/PubMed |
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| Keywords | I3S AmphIdent APHIS Wild‐ID photographic identification capture–recapture |
| Language | English |
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| SubjectTerms | 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 |
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| Title | Comparison of photo‐matching algorithms commonly used for photographic capture–recapture studies |
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