Instance-based Bird Species Identification with Undiscriminant Features Pruning

International audienceThis paper reports the participation of Inria to the audiobasedbird species identication challenge of LifeCLEF 2014 campaign.Inspired by recent works on ne-grained image classication, we introducean instance-based classication scheme based on the dense indexingof MFCC features and the pruning of the non-discriminant ones. To makesuch strategy scalable to the 30M of MFCC features extracted from thetens of thousands audio recordings of the training set, we used highdimensionalhashing techniques coupled with an ecient approximatenearest neighbors search algorithm with controlled quality. Further improvementsare obtained by (i) using a sliding classier with max pooling(ii) weighting the query features according to their semantic coherence(iii) making use of the metadata to lter incoherent species. Results showthe eectiveness of the proposed technique which ranked 3rd among the10 participating groups

PlantRT : a Distributed Recommendation Tool for Citizen Science

International audienceLes utilisateurs du Web 2.0 sont de gros producteurs de données diverses qu'ils stockent dans une grande variété de systèmes. Dans ce travail, nous nous concentrons sur le cas particulier des botanistes. En effet, établir une connaissance précise de l'identité, de la distribution géographique et de l'évolution des espèces vivantes est essentiel pour la pérennité de cette biodiversité, tout autant que pour l'espèce humaine. L'émergence des sciences citoyennes et des réseaux sociaux sont des outils supplémentaires favorisant la création de grandes communautés d'observateurs de la nature, qui ont commencé a produire d'énormes collections de données multimédias. Cependant, la complexité inhérente à la réalisation de ces collections provoque une certaine méfiance des utilisateurs, ces dernier ne souhaitant pas stocker leurs données sur un serveur central. Dans ce travail, nous avons réalisé un prototype multi-sites, où chaque site, peut représenter 1 à n utilisateurs permettant la recherche et la recommandation d'observations de plantes diversifiées à grand échelle

Floristic participation at LifeCLEF 2016 Plant Identification Task

International audienceThis 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

Instance-based Bird Species Identification with Undiscriminant Features Pruning

International audienceThis paper reports the participation of Inria to the audiobasedbird species identication challenge of LifeCLEF 2014 campaign.Inspired by recent works on ne-grained image classication, we introducean instance-based classication scheme based on the dense indexingof MFCC features and the pruning of the non-discriminant ones. To makesuch strategy scalable to the 30M of MFCC features extracted from thetens of thousands audio recordings of the training set, we used highdimensionalhashing techniques coupled with an ecient approximatenearest neighbors search algorithm with controlled quality. Further improvementsare obtained by (i) using a sliding classier with max pooling(ii) weighting the query features according to their semantic coherence(iii) making use of the metadata to lter incoherent species. Results showthe eectiveness of the proposed technique which ranked 3rd among the10 participating groups

Crowdsourcing Biodiversity Monitoring: How Sharing your Photo Stream can Sustain our Planet

International audienc

Shared nearest neighbors match kernel for bird songs identification -LifeCLEF 2015 challenge

International audienceThis paper presents a new fine-grained audio classification technique designed and experimented in the context of the LifeCLEF 2015 bird species identification challenge. Inspired by recent works on fine-grained image classification, we introduce a new match kernel based on the shared nearest neighbors of the low level audio features extracted at the frame level. To make such strategy scalable to the tens of millions of MFCC features extracted from the tens of thousands audio recordings of the training set, we used high-dimensional hashing techniques coupled with an efficient approximate nearest neighbors search algorithm with controlled quality. Further improvements are obtained by (i) using a sliding window for the temporal pooling of the raw matches (ii) weighting each low level feature according to the semantic coherence of its nearest neighbors. Results show the effectiveness of the proposed technique which ranked 2nd among the 7 research groups participating to the LifeCLEF bird challenge

A comparative study of fine-grained classification methods in the context of the LifeCLEF plant identification challenge 2015

International audienceThis paper describes the participation of Inria to the plant identification task of the LifeCLEF 2015 challenge. The aim of the task was to produce a list of relevant species for a large set of plant observations related to 1000 species of trees, herbs and ferns living in Western Europe. Each plant observation contained several annotated pictures with organ/view tags: Flower, Leaf, Fruit, Stem, Branch, Entire, Scan (exclusively of leaf). To address this challenge, we experimented two popular families of classification techniques, i.e. convolutional neural networks (CNN) on one side and fisher vectors-based discriminant models on the other side. Our results show that the CNN approach achieves much better performance than the fisher vectors. Beyond, we show that the fusion of both techniques, based on a Bayesian inference using the confusion matrix of each classifier, did not improve the results of the CNN alone

Unsupervised Individual Whales Identification: Spot the Difference in the Ocean

International audienceIdentifying organisms is a key step in accessing information related to the ecology of species. But unfortunately, this is difficult to achieve due to the level of expertise necessary to correctly identify and record living organisms. To try bridging this gap, enormous work has been done on the development of automated species identification tools such as image-based plant identification or audio recordings-based bird identification. Yet, for some groups, it is preferable to monitor the organisms at the individual level rather than at the species level. The automatizing of this problem has received much less attention than species identification. In this paper, we address the specific scenario of discovering humpack whales individuals in a large collections of pictures collected by nature observers. The process is initiated from scratch, without any knowledge on the number of individuals and without any training samples of these individuals. Thus, the problem is entirely unsupervised. To address it, we set up and experimented a scalable fine-grained matching system allowing to discover small rigid visual patterns in highly clutter background. The evaluation was conducted in blind in the context of the LifeCLEF evaluation campaign. Results show that the proposed system provides very promising results with regard to the difficulty of the task but that there is still room for improvements to reach higher recall and precision in the future

A look inside the Pl@ntNet experience

International audiencePl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps

ThePlantGame: Actively Training Human Annotators for Domain-specific Crowdsourcing

International audienceIn a typical citizen science/crowdsourcing environment, the contributorslabel items. When there are few labels, it is straightforwardto train contributors and judge the quality of their labels bygiving a few examples with known answers. Neither is true whenthere are thousands of domain-specic labels and annotators withheterogeneous skills. This demo paper presents an Active UserTraining framework implemented as a serious game called The-PlantGame. It is based on a set of data-driven algorithms allowingto (i) actively train annotators, and (ii) evaluate the quality of contributors’answers on new test items to optimize predictions