• Login
    View Item 
    •   Home
    • Faculty/ Researcher Works
    • Faculty/ Researcher Works
    • View Item
    •   Home
    • Faculty/ Researcher Works
    • Faculty/ Researcher Works
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of TUScholarShareCommunitiesDateAuthorsTitlesSubjectsGenresThis CollectionDateAuthorsTitlesSubjectsGenres

    My Account

    LoginRegister

    Help

    AboutPeoplePoliciesHelp for DepositorsData DepositFAQs

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Identification of Cichlid Fishes from Lake Malawi Using Computer Vision

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Identification of cichlid fishes ...
    Size:
    2.580Mb
    Format:
    PDF
    Download
    Genre
    Journal Article
    Date
    2013-10-25
    Author
    Joo, D
    Kwan, YS
    Song, J
    Pinho, C
    Hey, J
    Won, YJ
    Subject
    Animals
    Artificial Intelligence
    Cichlids
    Color
    Evolution, Molecular
    Fishes
    Fresh Water
    Humans
    Image Processing, Computer-Assisted
    Lakes
    Malawi
    Show allShow less
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5359
    
    Metadata
    Show full item record
    DOI
    10.1371/journal.pone.0077686
    Abstract
    Background:The explosively radiating evolution of cichlid fishes of Lake Malawi has yielded an amazing number of haplochromine species estimated as many as 500 to 800 with a surprising degree of diversity not only in color and stripe pattern but also in the shape of jaw and body among them. As these morphological diversities have been a central subject of adaptive speciation and taxonomic classification, such high diversity could serve as a foundation for automation of species identification of cichlids.Methodology/Principal Finding:Here we demonstrate a method for automatic classification of the Lake Malawi cichlids based on computer vision and geometric morphometrics. For this end we developed a pipeline that integrates multiple image processing tools to automatically extract informative features of color and stripe patterns from a large set of photographic images of wild cichlids. The extracted information was evaluated by statistical classifiers Support Vector Machine and Random Forests. Both classifiers performed better when body shape information was added to the feature of color and stripe. Besides the coloration and stripe pattern, body shape variables boosted the accuracy of classification by about 10%. The programs were able to classify 594 live cichlid individuals belonging to 12 different classes (species and sexes) with an average accuracy of 78%, contrasting to a mere 42% success rate by human eyes. The variables that contributed most to the accuracy were body height and the hue of the most frequent color.Conclusions:Computer vision showed a notable performance in extracting information from the color and stripe patterns of Lake Malawi cichlids although the information was not enough for errorless species identification. Our results indicate that there appears an unavoidable difficulty in automatic species identification of cichlid fishes, which may arise from short divergence times and gene flow between closely related species. © 2013 Joo et al.
    Citation to related work
    Public Library of Science (PLoS)
    Has part
    PLoS ONE
    ADA compliance
    For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/5341
    Scopus Count
    Collections
    Faculty/ Researcher Works

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Temple University Libraries | 1900 N. 13th Street | Philadelphia, PA 19122
    (215) 204-8212 | scholarshare@temple.edu
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.