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Structured Output Prediction with Hierarchical Loss Functions for Seafloor Imagery Taxonomic Categorization

  • Navid Nourani-Vatani
  • Roberto López-Sastre
  • Stefan Williams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)

Abstract

In this paper we study the challenging problem of seafloor imagery taxonomic categorization. Our contribution is threefold. First, we demonstrate that this task can be elegantly translated into a Structured SVM learning framework. Second, we introduce a taxonomic loss function in the structured output classification objective during learning that is shown to improve the performance over other loss functions. And third, we show how the Structured SVM can naturally deal with the problem of learning from data imbalance by scaling the cost of misclassification during the optimization. We present a thorough experimental evaluation using the challenging and publicly available Tasmania Coral Point Count dataset, where our models drastically outperform the state-of-the-art-results reported.

Keywords

Seafloor imagery Categorization Recognition Structured prediction 

Notes

Acknowledgements

The authors acknowledge the Australian National Research Program (NERP) Marine Biodiversity Hub for the taxonomical labeling and the Australian Centre for Field Robotics for gathering the image data.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Navid Nourani-Vatani
    • 1
  • Roberto López-Sastre
    • 2
  • Stefan Williams
    • 3
  1. 1.EREDMAN Truck and Bus AGMunichGermany
  2. 2.GRAMUniversity of AlcaláAlcalá de HenaresSpain
  3. 3.ACFRUniversity of SydneySydneyAustralia

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