Interactive Image Retrieval for Biodiversity Research
Alexander Freytag, Alena Schadt, Joachim Denzler · Lecture notes in computer science · 2015
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.