解决开放世界植物识别难题，应对未知物种干扰
Plant identification in an open-world (LifeCLEF 2016)
- 将植物识别设为开放集任务，需识别已知类并拒绝未知类
- 基于11万张图像、1000种西欧植物数据集进行评估
- 适合对开放世界识别与生物多样性监测感兴趣的读者
LifeCLEF植物识别挑战旨在评估大规模场景下的植物识别方法与系统，接近真实生物多样性监测条件。2016年版本使用超过11万张图像，涵盖1000种西欧植物，数据源自2011年启动的大众参与传感平台，现已有数万贡献者。相比往年，最大创新在于将识别任务设定为开放集识别问题——即系统需对未见类别保持鲁棒性。除对训练集中已知类别的分类外，主要挑战在于自动排除因未知类别导致的误报。本文详述了挑战的数据资源与评估流程，总结了各参赛团队的方法与系统，并分析了主要成果。
原文摘要 · Abstract (English)
The LifeCLEF plant identification challenge aims at evaluating plant identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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