CLASSIFICATION OF CHALLENGING MARINE IMAGERY

摘要

Covering over 70% of the Earth's surface and containing over 95% of the planet's water, the aquatic ecosystem has a great influence on many environmental functions. An indicator of the health of a marine habitat is its populations, estimated by taking underwater images and labeling various species. Designing an automated algorithm for this task is quite a challenge. Image quality tends to be low due to the dynamics of the water body. The diversity of shapes and motions among living plankton and non-living detritus are remarkable. We have applied two very different techniques from computer vision to the automatic labeling of tiny planktonic organisms. One is a common approach involving segmentation and calculations of statistical features. The other is inspired by the sophisticated visual processing in primates. Both achieved competitively high accuracies, comparable to general agreement among expert marine scientists. We found that a relatively simple biologically motivated system can be as effective as a more complicated classical schema in this domain.