Trainable superpixel segmentation
Salinas Colina, Josu
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Trainable Superpixel Segmentation is a plug-in developed for the ImageJ platform that aims at providing its users with the ability to train models to segment images by classifying superpixels using region-based image features. This project provides an underlying library that can be used independently, a graphic interface for ease of use and an evaluation protocol of the efficacy of the library. The evaluation of the developed library was conducted through a ten-fold cross-validation and the results were compared with those of the Trainable Weka Segmentation library.