Pinned Repositories
LifeCLEF
Task Overview Following the success of the four previous plant identification tasks (ImageCLEF 2011-13 ; LifeCLEF 2014), we are glad to organize this year a new challenge dedicated to botanical data. The task will be focused on tree, herbs and ferns species identification based on different types of images. Its main novelties compared to the last years will by : -"use of external resources" : it will be possible to use more external online resources (but strictly forbidden to used data from Tela Botanica website), as training data to enrich the provided one, - "species number" : the number of species (about 1 000 species), which is an important step towards covering the entire flora of a given region. Multi-image query The motivation of the task is to fit better with a real scenario where one user tries to identify a plant by observing its different organs, such as it has been demonstrated in [MAED2012]. Indeed, botanists usually observe simultaneously several organs like the leaves and the fruits or the flowers in order to disambiguate species which could be confused if only one organ were observed. Moreover, if only one organ is observed, such as the bark of a deciduous plant during winter where nothing else is observable, then the observation of this organ with several photos related to different point of views could be more informative than only one point of view. Thus, contrary to the 3 first years, the species identification task won't be image-centered but OBSERVATION-centered. The aim of the task is be to produce a list of relevant species for each observation of a plant of the test dataset, i.e. one or a set of several pictures related to a same event: one same person photographing several detailed views on various organs the same day with the same device with the same lightening conditions observing one same plant.
godot
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IAVoiceRecognition
LifeCLEF
Task Overview Following the success of the four previous plant identification tasks (ImageCLEF 2011-13 ; LifeCLEF 2014), we are glad to organize this year a new challenge dedicated to botanical data. The task will be focused on tree, herbs and ferns species identification based on different types of images. Its main novelties compared to the last years will by : -"use of external resources" : it will be possible to use more external online resources (but strictly forbidden to used data from Tela Botanica website), as training data to enrich the provided one, - "species number" : the number of species (about 1 000 species), which is an important step towards covering the entire flora of a given region. Multi-image query The motivation of the task is to fit better with a real scenario where one user tries to identify a plant by observing its different organs, such as it has been demonstrated in [MAED2012]. Indeed, botanists usually observe simultaneously several organs like the leaves and the fruits or the flowers in order to disambiguate species which could be confused if only one organ were observed. Moreover, if only one organ is observed, such as the bark of a deciduous plant during winter where nothing else is observable, then the observation of this organ with several photos related to different point of views could be more informative than only one point of view. Thus, contrary to the 3 first years, the species identification task won't be image-centered but OBSERVATION-centered. The aim of the task is be to produce a list of relevant species for each observation of a plant of the test dataset, i.e. one or a set of several pictures related to a same event: one same person photographing several detailed views on various organs the same day with the same device with the same lightening conditions observing one same plant.
epuscasu's Repositories
epuscasu/LifeCLEF
Task Overview Following the success of the four previous plant identification tasks (ImageCLEF 2011-13 ; LifeCLEF 2014), we are glad to organize this year a new challenge dedicated to botanical data. The task will be focused on tree, herbs and ferns species identification based on different types of images. Its main novelties compared to the last years will by : -"use of external resources" : it will be possible to use more external online resources (but strictly forbidden to used data from Tela Botanica website), as training data to enrich the provided one, - "species number" : the number of species (about 1 000 species), which is an important step towards covering the entire flora of a given region. Multi-image query The motivation of the task is to fit better with a real scenario where one user tries to identify a plant by observing its different organs, such as it has been demonstrated in [MAED2012]. Indeed, botanists usually observe simultaneously several organs like the leaves and the fruits or the flowers in order to disambiguate species which could be confused if only one organ were observed. Moreover, if only one organ is observed, such as the bark of a deciduous plant during winter where nothing else is observable, then the observation of this organ with several photos related to different point of views could be more informative than only one point of view. Thus, contrary to the 3 first years, the species identification task won't be image-centered but OBSERVATION-centered. The aim of the task is be to produce a list of relevant species for each observation of a plant of the test dataset, i.e. one or a set of several pictures related to a same event: one same person photographing several detailed views on various organs the same day with the same device with the same lightening conditions observing one same plant.