Automatic identification of planktonic foraminifera using convolutional neural networks based on two stage-segmentation algorithm
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Abstract:
Foraminifera are characterized by their small size, abundance, wide distribution, and quick evolution. They provide important information about the sedimentary environment in the ocean and are significant for biostratigraphic division and correlation in marine facies. Traditional identification of foraminifera requires experts with rich experi-ences and is very time consuming. Fossil identification by human faces lots of problems such as the lack of talents, the amount of work, and communication difficulties. Convolution neural network algorithm, having been widely applied in computer visual identification field, can solve these problems effectively. Guided by labeled Miocene planktonic fo-raminifera, we develop an image recognition system by integrating different visual angles of the fossils and convolu-tion neural network algorithm. The results indicate that, by applying the algorithm of two stage-segmentation, the fos-sils can be identified at generic level using the images of the umbilical, spiral, and edge views of the specimens. The identification accuracy of the computer model at generic level for the Miocene planktonic foraminifera can reach about 82%.
