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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lin Kang Yuanhao Gong Chenhui Yang Jinfei Luo Qiaoqi Luo Yahui Gao |
| Copyright Year | 2010 |
| Abstract | Marine phytoplanktons are unicellular algae with a variety of shapes and ornamentation, and they are widely used as indicators of marine ecosystem changes. A dual layer and hybrid classifier is presented in this study for phytoplankton recognition. The method is based on k-NN, SVM mechanisms and uses shape and texture information such as moments, geometric features and gray level co-occurrence matrix features. Each individual classifier has its own specific input feature and decision mechanism. The marine phytoplankton recognition experiment shows that the proposed classification method outperforms two well-known stand-alone classifiers, k-NN and SVM. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 754395 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424447121 |
| ISSN | 21517622 |
| DOI | 10.1109/ICBBE.2010.5517750 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Marine technology Support vector machines Shape Image databases Microscopy Ecosystems Taxonomy Support vector machine classification Algae Lakes |
| Content Type | Text |
| Resource Type | Article |
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