Strategies for Tackling the Class Imbalance Problem in Marine Image Classification

Langenkämper D, van Kevelaer R, Nattkemper TW (Accepted)
Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing.

Konferenzbeitrag | Angenommen | Englisch
 
Download
OA 1.71 MB
Abstract / Bemerkung
Abstract. Research of deep learning algorithms, especially in the field of convolutional neural networks (CNN), has shown significant progress. The application of CNNs in image analysis and pattern recognition has earned a lot of attention in this regard and few applications to classify a small number of common taxa in marine image collections have been reported yet. In this paper, we address the problem of class imbalance in marine image data, i.e. the common observation that 80%-90% of the data belong to a small subset of L′ classes among the total number of L observed classes, with L′ << L. A small number of methods to compensate for the class imbalance problem in the training step have been proposed for the common computer vision benchmark datasets. But marine image collections (showing for instance megafauna as considered in this study) pose a greater challenge as the observed imbalance is more extreme as habitats can feature a high biodiversity but a low species density. In this paper, we investigate the potential of various over-/undersampling methods to compensate for the class imbalance problem in marine imag- ing. In addition, five different balancing rules are proposed and analyzed to examine the extent to which sampling should be used, i.e. how many samples should be created or removed to gain the most out of the sam- pling algorithms. We evaluate these methods with AlexNet trained for classifying benthic image data recorded at the Porcupine Abyssal Plain (PAP) and use a Support Vector Machine as baseline classifier. We can report that the best of our proposed strategies in combination with data augmentation applied to AlexNet results in an increase of thirteen basis points compared to AlexNet without sampling. Furthermore, examples are presented, which show that the combination of oversampling and augmentation leads to a better generalization than pure augmentation.
Stichworte
class imbalance; CNN; marine imaging; deep learning; taxonomic classification
Erscheinungsjahr
2018
Konferenz
International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop
Konferenzort
Beijing
Konferenzdatum
2018-08-20 – 2018-08-24
Page URI
https://pub.uni-bielefeld.de/record/2921039

Zitieren

Langenkämper D, van Kevelaer R, Nattkemper TW. Strategies for Tackling the Class Imbalance Problem in Marine Image Classification. Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing.
Langenkämper, D., van Kevelaer, R., & Nattkemper, T. W. (Accepted). Strategies for Tackling the Class Imbalance Problem in Marine Image Classification. Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing.
Langenkämper, D., van Kevelaer, R., and Nattkemper, T. W. (Accepted).“Strategies for Tackling the Class Imbalance Problem in Marine Image Classification”. Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing.
Langenkämper, D., van Kevelaer, R., & Nattkemper, T.W., Accepted. Strategies for Tackling the Class Imbalance Problem in Marine Image Classification. Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing.
D. Langenkämper, R. van Kevelaer, and T.W. Nattkemper, “Strategies for Tackling the Class Imbalance Problem in Marine Image Classification”, Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing, Accepted.
Langenkämper, D., van Kevelaer, R., Nattkemper, T.W.: Strategies for Tackling the Class Imbalance Problem in Marine Image Classification. Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing (Accepted).
Langenkämper, Daniel, van Kevelaer, Robin, and Nattkemper, Tim Wilhelm. “Strategies for Tackling the Class Imbalance Problem in Marine Image Classification”. Presented at the International Conference on Pattern Recognition 2018, Computer Vision for Automated Analysis of Underwater Imagery Workshop, Beijing, Accepted.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-06T09:19:00Z
MD5 Prüfsumme
5912b0a710cdf0d0f8dccc78d6594312

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Suchen in

Google Scholar