| 基于PCA/ICA的海洋浮游生物识别 |
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| 引用本文： | 肖晓红,刘冬生,欧阳春娟. 基于PCA/ICA的海洋浮游生物识别[J]. 井冈山大学学报(自然科学版), 2017, 0(1): 60-67 |
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| 作者姓名： | 肖晓红 刘冬生 欧阳春娟 |
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| 作者单位： | 井冈山大学电子与信息工程学院, 江西, 吉安 343009;流域生态与地理环境监测国家测绘地理信息局重点实验室, 江西, 吉安 343009,井冈山大学电子与信息工程学院, 江西, 吉安 343009;流域生态与地理环境监测国家测绘地理信息局重点实验室, 江西, 吉安 343009,井冈山大学电子与信息工程学院, 江西, 吉安 343009;流域生态与地理环境监测国家测绘地理信息局重点实验室, 江西, 吉安 343009 |
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| 基金项目： | 国家自然科学基金项目（61462046）；江西省教育厅科学技术研究项目基金（GJJl4559；GJJl3553）；江西省科技厅自然科学项目基金（20151BAB207026）；井冈山大学博士科研启动项目（JZB1311）资助。 |
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| 摘 要： | 通过海洋浮游生物图像对浮游生物进行识别和分类,有助于合理地开发、管理、利用海洋资源。针对直接使用PCA或ICA形成的特征空间,不能达到各类别元素未能较好分离的问题,提出了一种浮游生物识别算法。采用PCA和ICA相结合的多层混合分类模型,提取WHOI数据集浮游生物样本特征,放入到分类模型中进行测试,并对不同距离度量的识别效果进行了比较。结果表明基于耦合度量的混合分类模型具有良好的分类性能。
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| 关 键 词： | 模式识别 浮游生物 主成分分析（PCA) 独立成分分析（ICA) 有监督学习 |
| 收稿时间： | 2016-07-12 |
| 修稿时间： | 2016-12-14 |
PCA/ICA BASED MARINE PLANKTON RECOGNITION |
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| XIAO Xiao-hong,LIU Dong-sheng and OUYANG-Chunjuan. PCA/ICA BASED MARINE PLANKTON RECOGNITION[J]. Journal of Jinggangshan University(Natural Sciences Edition), 2017, 0(1): 60-67 |
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| Authors: | XIAO Xiao-hong LIU Dong-sheng OUYANG-Chunjuan |
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| Affiliation: | School of Electronics and Information Engineering, Jinggangshan University, Ji''an, Jiangxi 343009, China;Key laboratory of watershed ecology and geographical environment monitoring, NASG, Ji''an, Jiangxi 343009, China,School of Electronics and Information Engineering, Jinggangshan University, Ji''an, Jiangxi 343009, China;Key laboratory of watershed ecology and geographical environment monitoring, NASG, Ji''an, Jiangxi 343009, China and School of Electronics and Information Engineering, Jinggangshan University, Ji''an, Jiangxi 343009, China;Key laboratory of watershed ecology and geographical environment monitoring, NASG, Ji''an, Jiangxi 343009, China |
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| Abstract: | The recognition and classification of marine plankton via its image can facilitate the development, management and utilization of marine resources. According to a direct implementation of PCA or ICA fail to separate the points in different classes in the eigenspace, we propose an algorithm for plankton recognition combining PCA and ICA to form a multi-layer classification mechanism. An experiment on plankton samples collected by WHOI is conducted to test the performance of our model, in which different features as well as measures of distance are explored. The result shows that the hybrid model using coupled measure outperforms others, and we further depict the shape of several sea planktons. |
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| Keywords: | pattern recognition marine plankton principal component analysis independent component analysis supervised learning |
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