This package contains the following two folders:
- Dataset: This folder contains three datasets (ASLO, Kaggle, and ZooScan).
- Code: This folder contains the code of our method, which classifies plankton images using multiple features combination via multiple kernel learning.
[1] Heidi M. Sosik and Robert J. Olson. Automated taxonomic classification of phytoplankton sampled with imaging‐in‐flow cytometry. Limnology and Oceanography: Methods, 5(2):204-216, 2007.
[2] Kaggle National Data Science Bowl: Predict ocean health, one plankton at a time. https://www.kaggle.com/c/datasciencebowl.
[3] Gaby Gorsky, Mark D Ohman, Marc Picheral, St ́ephane Gasparini, Lars Stemmann, Jean-Baptiste Romagnan, Alison Cawood, St ́ephane Pesant, Carmen Garc ́ıa-Comas, and Franck Prejger. Digital zooplankton image analysis using the ZooScan integrated system. Journal of Plankton Research, 32(3):285–303, 2010.
[4] Haibin Ling and David W Jacobs. Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2):286–299, 2007.
[5] Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh. Learning non-linear combinations of kernels. Advances in Neural Information Processing Systems, pages 396–404, 2009.
- Linux or OS X
- MATLAB
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The code includes five parts: 1 image preprocessing, 2 feature extraction, 3 feature selection, 4 cross validation, 5 multiple kernel learning. After downloading the code, you need to run them sequentially.
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If there are files with ".sh" as file extension in a folder, run these files in terminal firstly please. Then, run files with ".m" as file extension in MATLAB.
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If the last element of a filename is digit, it indicates that you need to run this file according to the order of digit. Conversely, if the last element of a filename isn't digit, this file can be run in no order.
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You need not run the files in "function" or "featuresFunction" folders by yourself, because these files are some functions or some dependent libraries.