期刊
PLOS ONE
卷 11, 期 2, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0148879
关键词
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资金
- National Science Foundation [NSF EF-1137396, NSF-DBI 1053036, NSF-DBI 1262547]
- National Center for Supercomputing Applications Institute for Advanced Computing Applications and Technologies (NCSA IACAT) Fellowship
- Research Experiences for Undergraduates (REU) from NSF Macrosystems Biology
- Direct For Biological Sciences [1262547] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Emerging Frontiers [1137396] Funding Source: National Science Foundation
- Div Of Biological Infrastructure [1262547] Funding Source: National Science Foundation
Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based pollen spotting model to segment pollen grains from the slide background. We next tested ARLO's ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems.
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