LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges

Automated multimedia identification tools are an emerging solution towards building accurate knowledge of the identity, the geographic distribution and the evolution of living plants and animals. Large and structured communities of nature observers 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 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 outcomes.

[1]  Jean-Christophe Lombardo,et al.  Unsupervised Individual Whales Identification: Spot the Difference in the Ocean , 2016, CLEF.

[2]  W. John Kress,et al.  Leafsnap: A Computer Vision System for Automatic Plant Species Identification , 2012, ECCV.

[3]  Nozha Boujemaa,et al.  The ImageCLEF 2012 Plant Identification Task , 2012, CLEF.

[4]  Charles E Taylor,et al.  Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models. , 2008, The Journal of the Acoustical Society of America.

[5]  Bogdan Ionescu,et al.  UPB HES SO @ PlantCLEF 2017: Automatic Plant Image Identification using Transfer Learning via Convolutional Neural Networks , 2017, CLEF.

[6]  Chee Seng Chan,et al.  LifeClef 2017 Plant Identification Challenge: Classifying Plants using Generic-Organ Correlation Features , 2017, CLEF.

[7]  Simon N. Stuart,et al.  2004 IUCN Red List of Threatened Species: A Global Species Assessment edited by Jonathan E.M. Baillie, Craig Hilton-Taylor & Simon N. Stuart (2004), xxiii + 191 pp., IUCN, Gland, Switzerland and Cambridge, UK. ISBN 2 8317 0826 5 (pbk), £18.50. , 2005, Oryx.

[8]  Linjie Xing,et al.  Marine Animal Detection and Recognition with Advanced Deep Learning Models , 2017, CLEF.

[9]  Masaki Aono,et al.  Residual Network with Delayed Max Pooling for Very Large Scale Plant Identification , 2017, CLEF.

[10]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[11]  Xiaoli Z. Fern,et al.  Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach. , 2012, The Journal of the Acoustical Society of America.

[12]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Jon Rosewell,et al.  Crowdsourcing the identification of organisms: A case-study of iSpot , 2015, ZooKeys.

[14]  Paul Roe,et al.  A toolbox for animal call recognition , 2012 .

[15]  Thomas G. Dietterich,et al.  The eBird enterprise: An integrated approach to development and application of citizen science , 2014 .

[16]  Itheri Yahiaoui,et al.  Interactive plant identification based on social image data , 2014, Ecol. Informatics.

[17]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[18]  Nozha Boujemaa,et al.  The imageCLEF plant identification task 2013 , 2013, MAED '13.

[19]  Hervé Glotin,et al.  LifeCLEF 2016: Multimedia Life Species Identification Challenges , 2016, CLEF.

[20]  Jinhai Cai,et al.  Sensor Network for the Monitoring of Ecosystem: Bird Species Recognition , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[21]  Hervé Glotin,et al.  LifeCLEF Bird Identification Task 2016: The arrival of Deep learning , 2016, CLEF.

[22]  Jonathan Krause,et al.  The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition , 2015, ECCV.

[23]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[24]  Sven Koitka,et al.  Recognizing Bird Species in Audio Files Using Transfer Learning , 2017, CLEF.

[25]  C. Marshall.  Encyclopedia of Life , 2008 .

[26]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[27]  Thomas Lidy,et al.  A Multi-modal Deep Neural Network approach to Bird-song Identication , 2017, CLEF.

[28]  Andreas Rauber,et al.  LifeCLEF Bird Identification Task 2017 , 2017, CLEF.

[29]  M. O'Neill,et al.  Automated species identification: why not? , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[30]  Berrin A. Yanikoglu,et al.  Plant Identification with Large Number of Classes: SabanciU-GebzeTU System in PlantCLEF 2017 , 2017, CLEF.

[31]  Palaniappan Mirunalini,et al.  Automatic Whale Matching System using Feature Descriptor , 2017, CLEF.

[32]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[33]  Pierre Bonnet,et al.  Plant Identification Based on Noisy Web Data: the Amazing Performance of Deep Learning (LifeCLEF 2017) , 2017, CLEF.

[34]  Thomas Hofmann,et al.  Audio Based Bird Species Identification using Deep Learning Techniques , 2016, CLEF.

[35]  Jean-Christophe Lombardo,et al.  Pl@ntNet app in the era of deep learning , 2017, ICLR.

[36]  Nozha Boujemaa,et al.  The ImageCLEF 2011 plant images classification task , 2011 .

[37]  Dah-Jye Lee,et al.  Contour matching for a fish recognition and migration-monitoring system , 2004, SPIE Optics East.

[38]  Stefan Kahl,et al.  Large-Scale Bird Sound Classification using Convolutional Neural Networks , 2017, CLEF.

[39]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[40]  Pierre Bonnet,et al.  Participation of INRIA & Pl@ntNet to ImageCLEF 2011 Plant Images Classification Task , 2011, CLEF.

[41]  Mario Lasseck.  Image-based Plant Species Identification with Deep Convolutional Neural Networks , 2017, CLEF.

[42]  Dávid Papp,et al.  Image Matching for Individual Recognition with SIFT, RANSAC and MCL , 2017, CLEF.

[43]  Sungbin Choi.  Fish Identification in Underwater Video with Deep Convolutional Neural Network: SNUMedinfo at LifeCLEF Fish task 2015 , 2015, CLEF.

[44]  Jiri Matas,et al.  Learning with Noisy and Trusted Labels for Fine-Grained Plant Recognition , 2017, CLEF.

[45]  Sven Koitka,et al.  Improving Model Performance for Plant Image Classification With Filtered Noisy Images , 2017, CLEF.

[46]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Hervé Goëau,et al.  A look inside the Pl@ntNet experience , 2015, Multimedia Systems.