Algal Recognition Based on Locally Linear Embedding and Support Vector Machine
Date Issued
2015
Date
2015
Author(s)
Guo, Ji
Abstract
This article is aimed to construct an effective system to implement algae recognition by using CUDA(compute unified device architecture)-based locally linear embedding(LLE) and support vector machine (SVMs approaches). In general, the previous pattern accuracy of algae recognition system is about 90% but it was lower for the recognition of some algae with irregular shapes in the natural water samples. Continuing Yang’s year study, I wanted to achieve a higher accuracy for the identification of the irregularly shaped algae. We used the images of algae captured from charge-coupled device (CCD) and only considered the algorithmic scheme. The algorithm of algal recognition system was constructed on Matlab. Features of algae were extracted by LLE, a manifold learning method, and then algae was classified by SVM, a classifier. Although the recognition accuracy for the unidentified objects is low, the accuracy for all the other algae is satisfactory. By deleting the unidentified objects first, the recognition rates for Chlorella, unidentified separatedCyanobacteria,Monoraphidium, Pediastrum,Cylindrospermum, Staurastrum are more than 80%. The recognition rates for unidentified agglomerated Cyanobacteria, Merismopedia, Microcystis are obviously lower, but they are still higher than the rates in Yang’s research. Besides, the k coefficient of the accuracy of recognition is 0.8099, which means that our recognition system is a method with high accuracy. Thirdly, LLE based on CUDA does accelerate the calculation. According to the results, this algal recognition system rlied on CUDA-based LLE and SVMs is proved to be more efficient and less time-consuming than the traditional method. Also, LLE with SVMs is better to recognize irregularly-shaped algae than explicit feature extraction method with ANN in natural water body. This system can be improved. First, removal of unidentified objects before classification of algae helps to achieve a higher accuracy rate, probably because the corresponding points of these objects do not lie in a manifold. We may also improve accuracy by modifying the existing LLE. In addition we might be able to adjust the depth of field and visual field of microscope and CCD to obtain clear enough images of appointed algae. Also, we need to dilute the samples to avoid the overlapping of several algae. Besides, it is time-consuming to compute the features if the size of sample set of test set is very large. Hence using CUDA to accelerate the process is essential and effective.
Subjects
algae recognition
LLE
SVMs
irregularly shaped algae
k coefficient
CUDA
Type
thesis
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