Plant identification: man vs. machine LifeCLEF 2014 plant identification challenge
This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of dive...
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| Published in | Multimedia tools and applications Vol. 75; no. 3; pp. 1647 - 1665 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.02.2016
Springer Nature B.V Springer Verlag |
| Series | LifeCLEF 2014 plant identification challenge |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems. |
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| AbstractList | This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing withexperienced botanists and clearly outperform beginners and inexperienced testsubjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems. This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems. |
| Author | Bonnet, Pierre Molino, Jean-François Joly, Alexis Vignau, Christel Barthélémy, Daniel Goëau, Hervé Champ, Julien Boujemaa, Nozha |
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| Copyright | Springer Science+Business Media New York 2015 Springer Science+Business Media New York 2016 Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1007/s11042-015-2607-4 |
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| Issue | 3 |
| Keywords | Visual plant identification Image analysis Human evaluation Digital data |
| Language | English |
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| PublicationSeriesTitle | LifeCLEF 2014 plant identification challenge |
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| SubjectTerms | Analysis Biodiversity Biodiversity and Ecology Botany Collaboration Computer Communication Networks Computer Science Computer Vision and Pattern Recognition Consortia Data Structures and Information Theory Datasets Environment and Society Environmental Sciences Flowers & plants Identification systems Image processing systems Image retrieval Life Sciences Multimedia Multimedia Information Systems Plant sciences Social networks Special Purpose and Application-Based Systems Studies Taxonomy Vegetal Biology Vision systems |
| Subtitle | LifeCLEF 2014 plant identification challenge |
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| Title | Plant identification: man vs. machine |
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