A Svm-Based Algorithm for Automatic Species Classification of a Marine Diatom Genus Coscinodiscus Ehrenberg

• Published in: 2010 4th International Conference on Bioinformatics and Biomedical Engineering pp. 1 - 6
• Main Authors: Luo, Jinfei, Luo, Qiaoqi, Gao, Yahui, Chen, Changping, Liang, Junrong, Yang, Chenhui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2010
• Subjects: Computational complexity; Error analysis; Feature extraction; Libraries; Pattern recognition; Robustness; Shape; Support vector machine classification; Support vector machines; Testing
• ISBN: 9781424447121; 1424447127
• ISSN: 2151-7614
• DOI: 10.1109/ICBBE.2010.5515840

Coscinodiscus Ehrenberg is a large and ecologically important diatom genus with plentiful species in marine phytoplankton and with a variety of round shapes and ornamentation. These properties can be measured by computer image pre-segmentation and feature extraction with threshold methods. However, it proves to be complicated task because of the high spatial variability of ornamentation properties. Researchers have shown a Teach-program and a number of library functions operating on sample image lists (SIL's) and operating on classifiers (CF's) to solve the problem. In this paper, we present Coscinodiscus Ehrenberg ornamentation classifier algorithm called support vector machines (SVMs) to derive a new set of SIL's and CF's. The principal purpose of SVMs is Coscinodiscus Ehrenberg images pattern recognition approach. A pattern is in this context always the SIL's contained in a sub-rectangle of some given (possibly larger) image. For the same classifier this sub-rectangle must always have the same dimensions, while the query image to be searched may be arbitrarily large. The training is done by preparing SIL's for the pattern taxa in question and feeding them to CF's created. Our classifier generation with preprocessing code optimization achieves a AAAAA preprocessing code, a 0.981 learning success, a 100% computational complexity. Train with SIL's achieves 212 samples, 17 taxa, a 0.472% error rate and Test with Query image searching achieves 253 samples, 17 taxa, a 15.81% error rate. The experiments demonstrate that the proposed method is very robust to the threshold segmentation and ornamentation feature extraction of Coscinodiscus Ehrenberg images, and is effective and useful for species classification of Coscinodiscus Ehrenberg.