Paper Info
Title
WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification.
Abstract
Planktonic organisms are of fundamental importance to marine ecosystems: they form the basis of the food web, provide the link between the atmosphere and the deep ocean, and influence global-scale biogeochemical cycles. Scientists are increasingly using imaging-based technologies to study these creatures in their natural habit. Images from such systems provide an unique opportunity to model and understand plankton ecosystems, but the collected datasets can be enormous. The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for example, is an emph{in situ} system that has been continuously imaging plankton since 2006. To date, it has generated more than 700 million samples. Manual classification of such a vast image collection is impractical due to the size of the data set. In addition, the annotation task is challenging due to the large space of relevant classes, intra-class variability, and inter-class similarity. Methods for automated classification exist, but the accuracy is often below that of human experts. Here we introduce WHOI-Plankton: a large scale, fine-grained visual recognition dataset for plankton classification, which comprises over 3.4 million expert-labeled images across 70 classes. The labeled image set is complied from over 8 years of near continuous data collection with the IFCB at the Marthau0027s Vineyard Coastal Observatory (MVCO). We discuss relevant metrics for evaluation of classification performance and provide results for a traditional method based on hand-engineered features and two methods based on convolutional neural networks.
Year
Venue
Field
2015
arXiv: Computer Vision and Pattern Recognition
Plankton,Creatures,Data collection,Data mining,Annotation,Convolutional neural network,Computer science,Visual recognition,Marine ecosystem,Artificial intelligence,Machine learning
DocType
Volume
Citations 
Journal
abs/1510.00745
4
PageRank 
References 
Authors
0.50
2
4
Name
Order
Citations
PageRank
Eric C. Orenstein140.50
Oscar Beijbom22249.94
Emily E. Peacock340.50
Heidi M. Sosik451.53