it will produce binary pixel classification. Both commands will use the same GUI but offer different feature options in their settings.īy default, the plugin starts with two classes, i.e. For 3D features, call the plugin under Plugins › Segmentation › Trainable Weka Segmentation 3D.

To use 2D features, you need to select the menu command Plugins › Segmentation › Trainable Weka Segmentation.

Trainable Weka Segmentation runs on any 2D or 3D image (grayscale or color). The Graphical User InterfaceĮxample of the first look of the plugin window when using it on a TEM image It provides the framework to use and, more important, compare any available classifier to perform image segmentation based on pixel classification. The main goal of this plugin is to work as a bridge between the Machine Learning and the Image Processing fields. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.

ease of use due to its graphical user interfaces.

a comprehensive collection of data preprocessing and modeling techniques.

portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform.

freely availability under the GNU General Public License.

As described on their wikipedia site, the advantages of Weka include: It contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. Weka (Waikato Environment for Knowledge Analysis) can itself be called from the plugin. The Trainable Weka Segmentation is a Fiji plugin that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations. Segmentation: it provides a labeled result based on the training of a chosen classifier. Weka: it makes use of all the powerful tools and classifiers from the latest version of Weka.

Trainable: this plugin can be trained to learn from the user input and perform later the same task in unknown (test) data. If you’d like to help, check out the how to help guide! The content of this page has not been vetted since shifting away from MediaWiki.

Weka jar file has the correct path