Fire images classification based on a handcraft approach

•The current study extracts higher-order features from fire images.•A new set of data is correctly labelled for classifying fire and non-fire.•Information-theoretic feature selection is adopted to minimize computational cost.•The SVM performs classification with an RBF kernel.•The model draws an overall accuracy of 96,21%, and a specificity of 97,99%.•The model draws an f-measure and g-mean values of 96,13% and 96,19% respectively.