The Experts below are selected from a list of 131667 Experts worldwide ranked by ideXlab platform
Alexis Joly - One of the best experts on this subject based on the ideXlab platform.
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Overview of LifeCLEF Plant Identification Task 2019: diving into Data Deficient Tropical Countries
2019Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:Automated Identification of Plants has improved consider-ably thanks to the recent progress in deep learning and the availabilityof training data. However, this profusion of data only concerns a few tensof thousands of species, while the planet has nearly 369K. The LifeCLEF2019 Plant Identification challenge (or ”PlantCLEF 2019”) was designedto evaluate automated Identification on the flora of data deficient regions.It is based on a dataset of 10K species mainly focused on the Guianashield and the Northern Amazon rainforest, an area known to have oneof the greatest diversity of Plants and animals in the world. As in theprevious edition, a comparison of the performance of the systems eval-uated with the best tropical flora experts was carried out. This paperpresents the resources and assessments of the challenge, summarizes theapproaches and systems employed by the participating research groups,and provides an analysis of the main outcomes.
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Deep learning for Plant Identification: how the web can compete with human experts
Biodiversity Information Science and Standards, 2018Co-Authors: Hervé Goëau, Alexis Joly, Pierre Bonnet, Mario Lasseck, Milan Sulc, Siang Thye HangAbstract:Automated Identification of Plants and animals has improved considerably in the last few years, in particular thanks to the recent advances in deep learning. In order to evaluate the performance of automated Plant Identification technologies in a sustainable and repeatable way, a dedicated system-oriented benchmark was setup in 2011 in the context of ImageCLEF (Goëau et al. 2011). Each year, since that time, several research groups participated in this large collaborative evaluation by benchmarking their image-based Plant Identification systems. In 2014, the LifeCLEF research platform (Joly et al. 2014) was created in the continuity of this effort so as to enlarge the evaluated challenges by considering birds and fishes in addition to Plants, and audio and video contents in addition to images. The 2017-th edition of the LifeCLEF Plant Identification challenge (Joly et al. 2017) is an important milestone towards automated Plant Identification systems working at the scale of continental floras with 10.000 Plant species living mainly in Europe and North America illustrated by a total of 1.1M images. Nowadays, such ambitious systems are enabled thanks to the conjunction of the dazzling recent progress in image classification with deep learning and several outstanding international initiatives, aggregating the visual knowledge on Plant species coming from the main national botanical institutes. The PlantCLEF Plant challenge that we propose to present at this workshop aimed at evaluating to what extent a large noisy training dataset collected through the web (then containing a lot of labelling errors) can compete with a smaller but trusted training dataset checked by experts. To fairly compare both training strategies, the test dataset was created from a third data source, the Pl@ntNet (Joly et al. 2015) mobile application that collects millions of Plant image queries all over the world. Due to the good results obtained at the 2017-th edition of the LifeCLEF Plant Identification challenge, the next big question is how far such automated systems are from the human expertise. Indeed, even the best experts are sometimes confused and/or disagree with each other when validating images of living organism. A multimedia data actually contains only partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncertainty and comparing it to the performance of automated systems is of high interest for both computer scientists and expert naturalists. This work reports an experimental study following this idea in the Plant domain. In total, 9 deep-learning systems implemented by 3 different research teams were evaluated with regard to 9 expert botanists of the French flora. The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise. This shows that automated Plant Identification systems are now mature enough for several routine tasks, and can offer very promising tools for autonomous ecological surveillance systems.
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Plant Identification in an Open-world (LifeCLEF 2016)
2016Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:The LifeCLEF Plant Identification challenge aims at evaluating Plant Identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 Plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the Identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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CLEF (Working Notes) - Plant Identification in an Open-world (LifeCLEF 2016)
2016Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:The LifeCLEF Plant Identification challenge aims at evaluating Plant Identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 Plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the Identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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Floristic participation at LifeCLEF 2016 Plant Identification Task
2016Co-Authors: Julien Champ, Hervé Goëau, Alexis JolyAbstract:This paper describes the participation of the Floristic consortium to the LifeCLEF 2016 Plant Identification challenge[18]. The aim of the task was to produce a list of relevant species for a large set of Plant images related to 1000 species of trees, herbs and ferns living in Western Europe, knowing that some of these images belonged to unseen categories in the training set like Plant species from other areas, horticultural Plants or even off topic images (people, keyboards, animals, etc). To address this challenge, we first experimented as a baseline, without any rejection procedure, a Convolutional Neural Network (CNN) approach based on a slightly modified GoogLeNet model. In a second run, we applied a simple rejection criteria based on probability threshold estimation on the output of the CNN, one for each species, for removing automatically species propositions judged irrelevant. In the third run, rather than definitely eliminating some species predictions with the risk to remove false negative propositions, we applied various attenuation factors in order to revise the probability distributions given by the CNN as confident score expressing how much a query was related or not to the known species. More precisely, for this last run we used the geographical information and several cohesion measures in terms of observation, "organ" tags and taxonomy (genus and family levels) based on a knn similarity search results within the training set.
Hervé Goëau - One of the best experts on this subject based on the ideXlab platform.
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Overview of LifeCLEF Plant Identification Task 2019: diving into Data Deficient Tropical Countries
2019Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:Automated Identification of Plants has improved consider-ably thanks to the recent progress in deep learning and the availabilityof training data. However, this profusion of data only concerns a few tensof thousands of species, while the planet has nearly 369K. The LifeCLEF2019 Plant Identification challenge (or ”PlantCLEF 2019”) was designedto evaluate automated Identification on the flora of data deficient regions.It is based on a dataset of 10K species mainly focused on the Guianashield and the Northern Amazon rainforest, an area known to have oneof the greatest diversity of Plants and animals in the world. As in theprevious edition, a comparison of the performance of the systems eval-uated with the best tropical flora experts was carried out. This paperpresents the resources and assessments of the challenge, summarizes theapproaches and systems employed by the participating research groups,and provides an analysis of the main outcomes.
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Deep learning for Plant Identification: how the web can compete with human experts
Biodiversity Information Science and Standards, 2018Co-Authors: Hervé Goëau, Alexis Joly, Pierre Bonnet, Mario Lasseck, Milan Sulc, Siang Thye HangAbstract:Automated Identification of Plants and animals has improved considerably in the last few years, in particular thanks to the recent advances in deep learning. In order to evaluate the performance of automated Plant Identification technologies in a sustainable and repeatable way, a dedicated system-oriented benchmark was setup in 2011 in the context of ImageCLEF (Goëau et al. 2011). Each year, since that time, several research groups participated in this large collaborative evaluation by benchmarking their image-based Plant Identification systems. In 2014, the LifeCLEF research platform (Joly et al. 2014) was created in the continuity of this effort so as to enlarge the evaluated challenges by considering birds and fishes in addition to Plants, and audio and video contents in addition to images. The 2017-th edition of the LifeCLEF Plant Identification challenge (Joly et al. 2017) is an important milestone towards automated Plant Identification systems working at the scale of continental floras with 10.000 Plant species living mainly in Europe and North America illustrated by a total of 1.1M images. Nowadays, such ambitious systems are enabled thanks to the conjunction of the dazzling recent progress in image classification with deep learning and several outstanding international initiatives, aggregating the visual knowledge on Plant species coming from the main national botanical institutes. The PlantCLEF Plant challenge that we propose to present at this workshop aimed at evaluating to what extent a large noisy training dataset collected through the web (then containing a lot of labelling errors) can compete with a smaller but trusted training dataset checked by experts. To fairly compare both training strategies, the test dataset was created from a third data source, the Pl@ntNet (Joly et al. 2015) mobile application that collects millions of Plant image queries all over the world. Due to the good results obtained at the 2017-th edition of the LifeCLEF Plant Identification challenge, the next big question is how far such automated systems are from the human expertise. Indeed, even the best experts are sometimes confused and/or disagree with each other when validating images of living organism. A multimedia data actually contains only partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncertainty and comparing it to the performance of automated systems is of high interest for both computer scientists and expert naturalists. This work reports an experimental study following this idea in the Plant domain. In total, 9 deep-learning systems implemented by 3 different research teams were evaluated with regard to 9 expert botanists of the French flora. The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise. This shows that automated Plant Identification systems are now mature enough for several routine tasks, and can offer very promising tools for autonomous ecological surveillance systems.
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Plant Identification in an Open-world (LifeCLEF 2016)
2016Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:The LifeCLEF Plant Identification challenge aims at evaluating Plant Identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 Plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the Identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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CLEF (Working Notes) - Plant Identification in an Open-world (LifeCLEF 2016)
2016Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:The LifeCLEF Plant Identification challenge aims at evaluating Plant Identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 Plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the Identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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Floristic participation at LifeCLEF 2016 Plant Identification Task
2016Co-Authors: Julien Champ, Hervé Goëau, Alexis JolyAbstract:This paper describes the participation of the Floristic consortium to the LifeCLEF 2016 Plant Identification challenge[18]. The aim of the task was to produce a list of relevant species for a large set of Plant images related to 1000 species of trees, herbs and ferns living in Western Europe, knowing that some of these images belonged to unseen categories in the training set like Plant species from other areas, horticultural Plants or even off topic images (people, keyboards, animals, etc). To address this challenge, we first experimented as a baseline, without any rejection procedure, a Convolutional Neural Network (CNN) approach based on a slightly modified GoogLeNet model. In a second run, we applied a simple rejection criteria based on probability threshold estimation on the output of the CNN, one for each species, for removing automatically species propositions judged irrelevant. In the third run, rather than definitely eliminating some species predictions with the risk to remove false negative propositions, we applied various attenuation factors in order to revise the probability distributions given by the CNN as confident score expressing how much a query was related or not to the known species. More precisely, for this last run we used the geographical information and several cohesion measures in terms of observation, "organ" tags and taxonomy (genus and family levels) based on a knn similarity search results within the training set.
Pierre Bonnet - One of the best experts on this subject based on the ideXlab platform.
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Overview of LifeCLEF Plant Identification Task 2019: diving into Data Deficient Tropical Countries
2019Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:Automated Identification of Plants has improved consider-ably thanks to the recent progress in deep learning and the availabilityof training data. However, this profusion of data only concerns a few tensof thousands of species, while the planet has nearly 369K. The LifeCLEF2019 Plant Identification challenge (or ”PlantCLEF 2019”) was designedto evaluate automated Identification on the flora of data deficient regions.It is based on a dataset of 10K species mainly focused on the Guianashield and the Northern Amazon rainforest, an area known to have oneof the greatest diversity of Plants and animals in the world. As in theprevious edition, a comparison of the performance of the systems eval-uated with the best tropical flora experts was carried out. This paperpresents the resources and assessments of the challenge, summarizes theapproaches and systems employed by the participating research groups,and provides an analysis of the main outcomes.
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Deep learning for Plant Identification: how the web can compete with human experts
Biodiversity Information Science and Standards, 2018Co-Authors: Hervé Goëau, Alexis Joly, Pierre Bonnet, Mario Lasseck, Milan Sulc, Siang Thye HangAbstract:Automated Identification of Plants and animals has improved considerably in the last few years, in particular thanks to the recent advances in deep learning. In order to evaluate the performance of automated Plant Identification technologies in a sustainable and repeatable way, a dedicated system-oriented benchmark was setup in 2011 in the context of ImageCLEF (Goëau et al. 2011). Each year, since that time, several research groups participated in this large collaborative evaluation by benchmarking their image-based Plant Identification systems. In 2014, the LifeCLEF research platform (Joly et al. 2014) was created in the continuity of this effort so as to enlarge the evaluated challenges by considering birds and fishes in addition to Plants, and audio and video contents in addition to images. The 2017-th edition of the LifeCLEF Plant Identification challenge (Joly et al. 2017) is an important milestone towards automated Plant Identification systems working at the scale of continental floras with 10.000 Plant species living mainly in Europe and North America illustrated by a total of 1.1M images. Nowadays, such ambitious systems are enabled thanks to the conjunction of the dazzling recent progress in image classification with deep learning and several outstanding international initiatives, aggregating the visual knowledge on Plant species coming from the main national botanical institutes. The PlantCLEF Plant challenge that we propose to present at this workshop aimed at evaluating to what extent a large noisy training dataset collected through the web (then containing a lot of labelling errors) can compete with a smaller but trusted training dataset checked by experts. To fairly compare both training strategies, the test dataset was created from a third data source, the Pl@ntNet (Joly et al. 2015) mobile application that collects millions of Plant image queries all over the world. Due to the good results obtained at the 2017-th edition of the LifeCLEF Plant Identification challenge, the next big question is how far such automated systems are from the human expertise. Indeed, even the best experts are sometimes confused and/or disagree with each other when validating images of living organism. A multimedia data actually contains only partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncertainty and comparing it to the performance of automated systems is of high interest for both computer scientists and expert naturalists. This work reports an experimental study following this idea in the Plant domain. In total, 9 deep-learning systems implemented by 3 different research teams were evaluated with regard to 9 expert botanists of the French flora. The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise. This shows that automated Plant Identification systems are now mature enough for several routine tasks, and can offer very promising tools for autonomous ecological surveillance systems.
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Plant Identification in an Open-world (LifeCLEF 2016)
2016Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:The LifeCLEF Plant Identification challenge aims at evaluating Plant Identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 Plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the Identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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CLEF (Working Notes) - Plant Identification in an Open-world (LifeCLEF 2016)
2016Co-Authors: Hervé Goëau, Pierre Bonnet, Alexis JolyAbstract:The LifeCLEF Plant Identification challenge aims at evaluating Plant Identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 Plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the Identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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Plant Identification: man vs. machine
Multimedia Tools and Applications, 2016Co-Authors: Pierre Bonnet, Julien Champ, Christel Vignau, Jean-françois Molino, Daniel Barthélémy, Hervé Goëau, Alexis Joly, Nozha BoujemaaAbstract: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.
Hamlyn G. Jones - One of the best experts on this subject based on the ideXlab platform.
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What Plant is that? Tests of automated image recognition apps for Plant Identification on Plants from the British flora
AoB PLANTS, 2020Co-Authors: Hamlyn G. JonesAbstract:There has been a recent explosion in development of image recognition technology and its application to automated Plant Identification, so it is timely to consider its potential for field botany. Nine free apps or websites for automated Plant Identification and suitable for use on mobile phones or tablet computers in the field were tested on a disparate set of 38 images of Plants or parts of Plants chosen from the higher Plant flora of Britain and Ireland. There were large differences in performance with the best apps identifying >50 % of samples tested to genus or better. Although the accuracy is good for some of the top-rated apps, for any quantitative biodiversity study or for ecological surveys, there remains a need for validation by experts or against conventional floras. Nevertheless, the better-performing apps should be of great value to beginners and amateurs and may usefully stimulate interest in Plant Identification and nature. Potential uses of automated image recognition Plant Identification apps are discussed and recommendations made for their future use.
Tian Fang Xu - One of the best experts on this subject based on the ideXlab platform.
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Review of Plant Identification Based on Image Processing
Archives of Computational Methods in Engineering, 2016Co-Authors: Zhaobin Wang, Huale Li, Tian Fang XuAbstract:Plant recognition is closely related to people’s life. The operation of the traditional Plant Identification method is complicated, and is unfavorable for popularization. The rapid development of computer image processing and pattern recognition technology makes it possible for computer’s automatic recognition of Plant species based on image processing. There are more and more researchers drawing their attention on the computer’s automatic Identification technology based on Plant images in recent years. Based on this, we have carried on a wide range of research and analysis on the Plant Identification method based on image processing in recent years. First of all, the research significance and history of Plant recognition technologies are introduced in this paper; secondly, the main technologies and steps of Plant recognition are reviewed; thirdly, more than 30 leaf features (including 16 shape features, 11 texture features, four color features), and then SVM was used to evaluate these features and their fusion features, and 8 commonly used classifiers are introduced in detail. Finally, the paper is ended with a conclusion of the insufficient of Plant Identification technologies and a prediction of future development.