Real-time Red Tide Algae Recognition using SVM and SVDD

摘要：

This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems. The classifier was required to reject non-object (non-target) algae and contaminative objects, which are not seen during training, thus producing a very difficult problem. In the proposed method, a support vector data description (SVDD),which offers good rejection ability, was trained to reject the contaminative objects and unknown algae and a support vector machine (SVM) was used to classify the algae to taxonomic categories. Our approach achieved greater 90％ accuracy on a collection of algal images. The test on contaminated algal image set (contains unknown algae and non-algae objects, such as sands) also demonstrated promising results.

关键词： red tide alga support vector machine feature extraction

作者: Jiang Tao Wang Cheng Wang Boliang Xie Jiezhen Jiao Nianzhi Luo Tingwei

作者单位: School of Electronic Science and Engineering National University of Defense Technology,Changsha, Hun School of Electronic Science and Engineering National University of Defense Technology, Changsha,Hun Department of Computer Science and Technology Xiamen University Xiamen, Fujian, China, 361005 Department of Computer Science and Technology Xiamen University Xiamen, Fujian, China,361005 State Key Laboratory of Marine Environmental Sciences Xiamen University Xiamen, Fujian, China, 36100

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 602-606

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）