Red tide algae classification using SVM-SNP and semi-supervised FCM

In this paper, a novel approach for classifying algal images was presented, which is used in flow-cytometry-based real-time red tide monitoring system. Firstly, an ensemble of support vector machines (SVMs) was trained and the test samples were labeled by them based on the summation of negative prob...

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Bibliographic Details
Published in:2010 2nd International Conference on Education Technology and Computer Vol. 1; pp. V1-389 - V1-392
Main Authors: Lili Xu, Tao Jiang, Jiezhen Xie, Shaoping Zheng
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2010
Subjects:
Algae
classifier
Clustering algorithms
fuzzy c-means (FCM)
Image classification
Kernel
Monitoring
Real time systems
red tide alga
Support vector machine classification
Support vector machines
SVM-SNP
Testing
Tides
ISBN:9781424463671, 142446367X
ISSN:2155-1812
Online Access:Get full text
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Summary:In this paper, a novel approach for classifying algal images was presented, which is used in flow-cytometry-based real-time red tide monitoring system. Firstly, an ensemble of support vector machines (SVMs) was trained and the test samples were labeled by them based on the summation of negative probability (SNP). Secondly, those samples most likely mistakenly labeled were picked out and re-labeled by semi-supervised fuzzy c-means (FCM) clustering algorithm. Experiments show that this new method improves the accuracy of algal images classification for the same subject with SVMs of different kernels.
ISBN:9781424463671
142446367X
ISSN:2155-1812
DOI:10.1109/ICETC.2010.5529223