Computationally Efficient Five-Class Image Classifier Based on Venn Predictors

This article shows the computational efficiency of an image classifier based on a Venn predictor with the nearest centroid taxonomy. It has been applied to the automatic classification of the images acquired by the Thomson Scattering diagnostic of the TJ-II stellarator. The Haar wavelet transform is used to reduce the image dimensionality. The average time per image to classify 1144 examples (in an on-line learning setting) is 0.166 ms. The classification of the last image takes 187 ms.