LifeCLEF 2016: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcom

Underwater Fish Detection using Deep Learning for Water Power Applications

Clean energy from oceans and rivers is becoming a reality with the development of new technologies like tidal and instream turbines that generate electricity from naturally flowing water. These new technologies are being monitored for effects on fish and other wildlife using underwater video. Methods for automated analysis of underwater video are needed to lower the costs of analysis and improve accuracy. A deep learning model, YOLO, was trained to recognize fish in underwater video using three very different datasets recorded at real-world water power sites. Training and testing with examples from all three datasets resulted in a mean average precision (mAP) score of 0.5392. To test how well a model could generalize to new datasets, the model was trained using examples from only two of the datasets and then tested on examples from all three datasets. The resulting model could not recognize fish in the dataset that was not part of the training set. The mAP scores on the other two datasets that were included in the training set were higher than the scores achieved by the model trained on all three datasets. These results indicate that different methods are needed in order to produce a trained model that can generalize to new data sets such as those encountered in real world applications.Comment: Accepted at CSCI 201

Are multimedia identification tools biodiversity-friendly?

This paper discusses the results of the LifeCLEF 2014 multimedia identification challenges with regards to the requirements of real-world ecological surveillance systems. In particular, we study the identification performances of the evaluated systems as a function of the ordinariness or rarity of the species in the dataset. This allows us to assess the ability of the underlying methods to be robust to heavily tailed distributions such as the ones encountered in real-world collections of life observations. Results show that all methods are more or less affected by the long-tail curse but that the best methods making use of classifiers with good discrimi- nation capacities do resist the phenomenon pretty well

LifeCLEF: Multimedia Life Species Identification

EMR co-located with ACM International Conference on Multimedia Retrieval (ICMR)International audienceBuilding accurate knowledge of the identity, the geographicdistribution and the evolution of living species is essentialfor a sustainable development of humanity as well as forbiodiversity conservation. In this context, using multimediaidentication tools is considered as one of the most promisingsolution to help bridging the taxonomic gap. With therecent advances in digital devices/equipment, network bandwidthand information storage capacities, the production ofmultimedia big data has indeed become an easy task. In parallel,the emergence of citizen sciences and social networkingtools has fostered the creation of large and structured communitiesof nature observers (e.g. eBird, Xeno-canto, TelaBotanica, etc.) that have started to produce outstandingcollections of multimedia records. Unfortunately, the performanceof the state-of-the-art multimedia analysis techniqueson such data is still not well understood and is far fromreaching the real world's requirements in terms of identi-cation tools. The LifeCLEF lab proposes to evaluate thesechallenges around 3 tasks related to multimedia informationretrieval and ne-grained classication problems in 3 livingworlds. Each task is based on large and real-world data andthe measured challenges are dened in collaboration withbiologists and environmental stakeholders in order to reflect realistic usage scenarios

LifeCLEF 2015: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridging the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g. eBird, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipments have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching the real world’s requirements. The LifeCLEF lab proposes to evaluate these challenges around three tasks related to multimedia information retrieval and fine-grained classification problems in three living worlds. Each task is based on large and real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders in order to reflect realistic usage scenarios. This paper presents more particularly the 2014 edition of LifeCLEF, i.e. the pilot one. For each of the three tasks, we report the methodology and the datasets as well as the official results and the main outcomes

LifeCLEF Bird Identification Task 2014

International audienceThe LifeCLEF bird identification task provides a testbed for a system-oriented evaluation of 501 bird species identification. The main originality of this data is that it was specifically built through a citizen science initiative conducted by Xeno-Canto, an international social net-work of amateur and expert ornithologists. This makes the task closer to the conditions of a real-world application than previous, similar ini-tiatives. This overview presents the resources and the assessments of the task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of ten groups from seven countries and with a total of twenty-nine runs submitted, involving distinct and original methods, this first year task confirms the interest of the audio retrieval community for biodiver-sity and ornithology, and highlights further challenging studies in bird identification

LifeCLEF Bird Identification Task 2015

International audienceThe LifeCLEF bird identification task provides a testbed for a system-oriented evaluation of 999 bird species identification. The main originality of this data is that it was specifically built through a citizen science initiative conducted by Xeno-Canto, an international social network of amateur and expert ornithologists. This makes the task closer to the conditions of a real-world application than previous, similar initiatives. This overview presents the resources and the assessments of the task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results

Applying Object Detection to Marine Data and Exploring Explainability of a Fully Convolutional Neural Network Using Principal Component Analysis

With the rise of focus on man made changes to our planet and wildlife therein, more and more emphasis is put on sustainable and responsible gathering of resources. In an effort to preserve maritime wildlife the Norwegian government decided to create an overview of the presence and abundance of various species of marine lives in the Norwegian fjords and oceans. The current work evaluates the possibility of utilizing machine learning methods in particular the You Only Look Once version 3 algorithm to detect fish in challenging conditions characterized by low light, undesirable algae growth and high noise. It was found that the algorithm trained on images collected during the day time under natural light could detect fish successfully in images collected during night under artificial lighting. The overall average precision score of 88% was achieved. Later principal component analysis was used to analyze the features learned in different layers of the network. It is concluded that for the purpose of object detection in specific application areas, the network can be considerably simplified since many of the feature detector turns our to be redundant.acceptedVersio