The week's pick
Reseach Article
Classifying Bacterial Species using Computer Vision and Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 151 - Number 8 |
| Year of Publication: 2016 |
| Authors: Venkatesh Vijaykumar |
10.5120/ijca2016911851
|
Venkatesh Vijaykumar . Classifying Bacterial Species using Computer Vision and Machine Learning. International Journal of Computer Applications. 151, 8 ( Oct 2016), 23-26. DOI=10.5120/ijca2016911851
Abstract
The study embodied in this paper, aims at making use of machine learning and computer vision algorithms in order to reliably identify the species of bacteria, from their microscopic images. The study has taken into consideration three of the most commonly occurring species of bacteria that are clinically important. The work shown further in this study can be extended to a larger number of bacteria species. The study makes use of the Speeded Up Robust Features or SURF algorithm for detecting image keypoints. The artificial intelligence classifier makes use of these keypoint vectors as its input variable. It is noteworthy that the images used are taken at the gram staining stage of bacterial identification, in order to minimize any biases in the dataset owing to a variance in staining technique, although the feature detector invariably grayscales the image prior to keypoint computation. The paper follows the study from the conception of the idea, to the formulating of the algorithm, the training and testing of the same.
References
- Mohamad, N. A., Jusoh, N. A., Htike, Z. Z., and Win, S. L. 2014. Bacteria Identification From Microscopic Morphology: A Survey. International Journal on Soft Computing, Artificial Intelligence and Applications.
- Mohamad, N. A., Jusoh, N. A., Htike, Z. Z., and Win, S. L. 2014. Bacteria Identification from Microscopic Morphology Using Naive Bayes. International Journal of Computer Science, Engineering and Information Technology.
- Rennie, Jason D. M., Shih, Lawrence., Teevan , Jaime., Karger, David R. 2003. Tackling the Poor Assumptions of Naive Bayes Text Classifiers, International Conference on Machine Learning, Washington DC
- Berend, Daniel; Kontorovich, Aryeh. 2015. A Finite Sample Analysis of the Naive Bayes Classifier, Journal of Machine Learning Research
- Lowe, David G. 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision.
- Bay, Herbert., Tuytelaars, Tinne., and Van Gool, Luc. 2006. SURF: Speeded Up Robust Features. Graz, Austria : Springer-Verlag, European Conference on Computer Vision.
- Juan, Luo., Gwun, Oubong. 2009. A Comparison of SIFT, PCA-SIFT and SURF, International Journal of Image Processing.
- Bradski, G. 2000. The OpenCV Library, Dr. Dobb's Journal of Software Tools.
- Pal, Sankar K and Mitra, Sushmita. 1992. Multilayer Perceptron, Fuzzy Sets, and Classification. IEEE Transactions on Neural Networks.
- Kingma, D. P., and Ba, J. L. 2015. ADAM: A Method for Stochastic Optimization, International Conference on Learning Representations, San Diego.
- Richardson, Elad., Herskovitz , Rom., Ginsburg , Boris., Zibulevsky , Michael. 2016. SEBOOST – Boosting Stochastic Learning Using Subspace Optimization Techniques, eprint arXiv:1609.00629
- Kearns, Michael and Ron, Dana. 1999. Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation. Neural Computation. s.l.: Massachusetts Institute of Technology.
- Vehtari, Aki., Gelman, Andrew., Gabry, Jonah. 2016. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC, eprint arXiv:1507.04544v5.
Index Terms
Keywords