Supervised Image Classification With Bio7, R And ImageJ

November 27, 2015
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(This article was first published on » R, and kindly contributed to R-bloggers)

27.11.2015

I created a new Bio7 example which demonstrates how to classify an image with Bio7, ImageJ and R.

classifyimage

For the classification I used the “randomForest” R package and an image example of ImageJ so you can reproduce the example quite easily. I made the example script as easy as possible and trained the classifier with 64 trees by default (see literature below). Not shown in the video is the procedure to control the prediction of the trained classifier with test data. You can find a simple script in the repository, too, which uses a method of the powerful ‘caret’ package.

Video:

If you have some recommendations of how to effectively use a decent classifier for image classification (e.g., which classifier is well suited for images, which tuning parameters are useful in this context, which additional signatures could be used, etc.) I would be happy to hear.

References:

Fernández-Delgado, M., Cernadas, E., Barro, S. & Amorim, D. Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? Journal of Machine Learning Research 15, 3133–3181 (2014).

Oshiro, T. M., Perez, P. S. & Baranauskas, J. A. in Machine Learning and Data Mining in Pattern Recognition (ed. Perner, P.) 154–168 (Springer Berlin Heidelberg, 2012).

Latinne, P., Latinne, P., Debeir, O. & Decaestecker, C. Limiting the Number of Trees in Random Forests. IN PROCEEDINGS OF MCS 2001, LNCS 2096, 2001.

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