75. leveraging context to improve machine learning classifications of marine zooplankton
Name: Jeffrey Scott Ellen
Grad Year: 2017
Deep Learning has led to many recent breakthroughs in automated recognition of diverse types of objects. However current out-of-the-box deep learning architectures have not performed as well with some types of digital images, including those of zooplankton. Current algorithms can only look for patterns within the pixels presented in the image that was captured. In this work, we investigate techniques for providing contextual metadata to Convolutional Neural Network algorithms applied to digital images acquired with two different digital imaging devices: our new Zooglider, and our ZooScan. We augment pixels with geometric measurements, geotemporal context, and hydrographic context then examine the effects on the algorithm's accuracy. We also compare the efficacy of Deep Learning classification with more conventional feature-based algorithms (e.g. Support Vector Machine and Neural Network). We suggest that our results are not unique to zooplankton imagery, but this approach is also translatable to other shape-based machine learning tasks.
Industry Application Area(s)
Life Sciences/Medical Devices & Instruments | Software, Analytics | Oceanography