Journal
2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017)
Volume -, Issue -, Pages 1082-1088Publisher
IEEE
DOI: 10.1109/WACV.2017.125
Keywords
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Funding
- National Science Foundation's BIGDATA Initiative [NSF IIS 15-46351]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1546351] Funding Source: National Science Foundation
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Studying marine plankton is critical to assessing the health of the world's oceans. To sample these important populations, oceanographers are increasingly using specially engineered in situ digital imaging systems that produce very large data sets. Most automated annotation efforts have considered data from individual systems in isolation. This is predicated on the assumption that the images from each system are so different that there would be little benefit to considering out-of-domain data. Meanwhile, in the computer vision community, much effort has been dedicated to understanding how using out-of-domain images can improve the performance of machine classifiers. In this paper, we leverage these advances to evaluate how well weights transfer between Convolutional Neural Networks (CNNs) trained on data from two radically different plankton imaging systems. We also examine the utility of CNNs as feature extractors on a third unique plankton data set. Our results indicate that these data sets are perhaps more similar in the eyes of a machine classifier than previously assumed. Further, these tests underscore the value of using the rich feature representations learned by CNNs to classify data in vastly different domains.
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