a confusion matrix $CM$, that is, a square matrix whose dimension is equal to the number of classes in the dataset. For the row $i$ in $CM$, the element $CM(i,i)$ gives the number of items which have been correctly classified as elements of the class $i$; similarly, elements $CM(i,j)$, with $j\neq i$, count the items which have been misclassified, resulting as elements of the class $j$ rather than elements of the class $i$. Similarly, the tier image $TI$ visualizes the matches of the NN, FT and ST. The value of the element $TI(i,j)$ is: *black* if $j$ is the NN of $i$, *red* if $j$ is among the $(|C|-1)$ top matches (FT) and *blue* if $j$ is among the $2(|C|-1)$ top matches (ST). For an ideal classification matrix, $CM$ becomes the diagonal matrix while the $TI$ clusters the black/red square pixels on the diagonal.

Results {#ss:ris_eval}
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In this Section we discuss the retrieval and classification performance of the edgeLBP. For simplicity, we report only the results obtained with the Bhattacharyya distance because in our experiments it performs better than the other distances considered. We performed multiple runs with different settings, changing the number ($N_r$) of rings and the number 