Interactive Image Retrieval for Biodiversity Research

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

On a daily basis, experts in biodiversity research are confronted with the challenging task of classifying individuals to build statistics over their distributions, their habitats, or the overall biodiversity. While the number of species is vast, experts with affordable time-budgets are rare. Image retrieval approaches could greatly assist experts: when new images are captured, a list of visually similar and previously collected individuals could be returned for further comparison. Following this observation, we start by transferring latest image retrieval techniques to biodiversity scenarios. We then propose to additionally incorporate an expert’s knowledge into this process by allowing him to select must-have-regions. The obtained annotations are used to train exemplar-models for region detection. Detection scores efficiently computed with convolutions are finally fused with an initial ranking to reflect both sources of information, global and local aspects. The resulting approach received highly positive feedback from several application experts. On datasets for butterfly and bird identification, we quantitatively proof the benefit of including expert-feedback resulting in gains of accuracy up to \(25\,\%\) and we extensively discuss current limitations and further research directions.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Alexander Freytag
    • 1
    • 2
  • Alena Schadt
    • 1
  • Joachim Denzler
    • 1
    • 2
  1. 1.Computer Vision GroupFriedrich Schiller University JenaJenaGermany
  2. 2.Michael Stifel Center JenaJenaGermany

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