FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891778802> ?p ?o ?g. }

• W2891778802 abstract "Summary Taxonomic experts classify millions of specimens, but this is very time-consuming and therefore expensive. Image analysis is a way to automate identification and was previously done at Naturalis Biodiversity Center for slipper orchids (Cypripedioideae) by the program ‘OrchID’. This program operated by extracting a pre-defined number of features from images, and these features were used to train artificial neural networks (ANN) to classify out-of-sample images. This program was extended to work for a collection of Javanese butterflies, donated to Naturalis by the Van Groenendael-Krijger Foundation. Originally, for the orchids, an image was divided into a pre-defined number of horizontal and vertical bins and the mean blue-green-red values of each bin were calculated (BGR method) to obtain image features. In the extended implementation, characteristic image features were extracted using the SURF algorithm implemented in OpenCV and clustered with the BagOfWords method (SURF-BOW method). In addition, a combination of BGR- and SURF-BOW was implemented to extract both types of features in a single dataset (BGR-SURF method). A selection of the butterfly and orchid images was made to create datasets with at least 5 and at most 50 specimens per species. The SURF-BOW and BGR-SURF methods were applied to both selected datasets, and the original BGR method was applied to the selected butterfly dataset. PCA plots were made to inspect visually how well the applied methods discriminated among the species. For the butterflies, both genus and species appeared to cluster together in the plots of the SURF-BOW method. However, no obvious clustering was noticeable for the orchid plots. The performance of the ANNs was validated by a stratified k -fold cross validation. For the butterflies, the BGR-SURF method scored best with an accuracy of 77%, against 71% for the SURF-BOW method and 66% for the BGR method, all for chained genus and species prediction with k = 10. The new methods could not improve the accuracy of the orchid classification with k = 10, which was 75% on genus, 52% on genus and section and 48% on genus, section and species in the original framework and now less than 25% for all. The validation results also showed that at least about 15 specimens per species were necessary for a good prediction with the SURF-BOW method. The BGR-SURF method was found to be the best of these methods for butterflies, but the original BGR method was best for the slipper orchids. In the future these methods may be tested with other datasets, for example with mosquitoes. In addition, other classifiers may be tested for better performance, like support vector machines." @default.
• W2891778802 created "2018-09-27" @default.
• W2891778802 creator A5022451862 @default.
• W2891778802 creator A5053461897 @default.
• W2891778802 date "2018-09-04" @default.
• W2891778802 modified "2023-10-14" @default.
• W2891778802 title "Image analysis for taxonomic identification of Javanese butterflies" @default.
• W2891778802 cites W1677409904 @default.
• W2891778802 cites W1989697277 @default.
• W2891778802 cites W1993164261 @default.
• W2891778802 cites W2059895429 @default.
• W2891778802 cites W2107034620 @default.
• W2891778802 cites W2143599142 @default.
• W2891778802 cites W2146292423 @default.
• W2891778802 cites W2149259880 @default.
• W2891778802 cites W2408288218 @default.
• W2891778802 cites W2433456020 @default.
• W2891778802 cites W2468512322 @default.
• W2891778802 cites W2515106546 @default.
• W2891778802 cites W3132826558 @default.
• W2891778802 doi "https://doi.org/10.1101/408146" @default.
• W2891778802 hasPublicationYear "2018" @default.
• W2891778802 type Work @default.
• W2891778802 sameAs 2891778802 @default.
• W2891778802 citedByCount "1" @default.
• W2891778802 countsByYear W28917788022021 @default.
• W2891778802 crossrefType "posted-content" @default.
• W2891778802 hasAuthorship W2891778802A5022451862 @default.
• W2891778802 hasAuthorship W2891778802A5053461897 @default.
• W2891778802 hasBestOaLocation W28917788021 @default.
• W2891778802 hasConcept C116834253 @default.
• W2891778802 hasConcept C153180895 @default.
• W2891778802 hasConcept C154945302 @default.
• W2891778802 hasConcept C18903297 @default.
• W2891778802 hasConcept C2778605236 @default.
• W2891778802 hasConcept C41008148 @default.
• W2891778802 hasConcept C86803240 @default.
• W2891778802 hasConceptScore W2891778802C116834253 @default.
• W2891778802 hasConceptScore W2891778802C153180895 @default.
• W2891778802 hasConceptScore W2891778802C154945302 @default.
• W2891778802 hasConceptScore W2891778802C18903297 @default.
• W2891778802 hasConceptScore W2891778802C2778605236 @default.
• W2891778802 hasConceptScore W2891778802C41008148 @default.
• W2891778802 hasConceptScore W2891778802C86803240 @default.
• W2891778802 hasLocation W28917788021 @default.
• W2891778802 hasOpenAccess W2891778802 @default.
• W2891778802 hasPrimaryLocation W28917788021 @default.
• W2891778802 hasRelatedWork W2094644012 @default.
• W2891778802 hasRelatedWork W2118558792 @default.
• W2891778802 hasRelatedWork W2118645886 @default.
• W2891778802 hasRelatedWork W2134727343 @default.
• W2891778802 hasRelatedWork W2392027047 @default.
• W2891778802 hasRelatedWork W2760085659 @default.
• W2891778802 hasRelatedWork W2904927891 @default.
• W2891778802 hasRelatedWork W2980300825 @default.
• W2891778802 hasRelatedWork W3012978760 @default.
• W2891778802 hasRelatedWork W4283328168 @default.
• W2891778802 isParatext "false" @default.
• W2891778802 isRetracted "false" @default.
• W2891778802 magId "2891778802" @default.
• W2891778802 workType "article" @default.