By OCAdmin in Research highlights on

Associate professor Chih-hao Hsieh from the Institute of Oceanography, NTU, leads his postdoc and student to develop an efficient semi-automatic zooplankton classification system. The results are published in the November issue of Marine Ecology Progress Series in 2012. This approach was successfully applied in the East China Sea ecosystem. In particular, the semi-automated approach achieves significant improvement in recognition accuracy for rare categories, thereby improving the estimates of taxa richness. This approach can make up for deficiencies of current automated zooplankton classifiers and facilitates an efficient semi-automated zooplankton classification, which may have a broad application in environmental monitoring and ecological research.

Figure 1. The ZooScan system and classified zooplankton images.

Figure 2. The statistical procedure to estimate classification confidence in order to achieve the semi-automatic classification.

Reference: Ye, L., C. Y. Chang, and C. H. Hsieh (2011) Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation. Marine Ecology Progress Series. 441: 185-196