\[fig:ar\_cmti\], along with the relative Tier Images. The number of models in this dataset is too small to consider meaningful the other evaluation measures.

The edgeLBP achieves good retrieval and classification results for most classes. We observed, as expected, that the correctness of the classification is mainly driven by the size of $R$, rather then $P$ and $N_r$. As a final note, we tested our algorithm using gray scale values as $h$ function: the results obtained with it were pretty much the same as those obtained with $h=L$. We think that this is due to which information both $L$ of CieLAB color space and the gray scale encodes.

  ------------------------------------------- ------------------------------------------- ------------------------------------------- -------------------------------------------
                                                                                                                                      
   ![image](pic/ar_cm_run1.png){width="3cm"}   ![image](pic/ar_ti_run1.png){width="3cm"}   ![image](pic/ar_cm_run3.png){width="3cm"}   ![image](pic/ar_ti_run3.png){width="3cm"}
  ------------------------------------------- ------------------------------------------- ------------------------------------------- -------------------------------------------

       Parameter Settings         NN      FT      ST
  ----------------------------- ------- ------- -------
   $P:15, N_r:5, R_{max}:0,2$    0.775   0.789     1
   $P:15, N_r:5, R_{max}:0,3$    0.75    0.811   0.989
   $P:15, N_r:5, R_{max}:0,5$    0.75    0.711   0.889
   $P:15, N_r:5, R_{max}:0,7*$   0.725   0.667   0.756
   $P:12, N_r:7, R_{max}:0,5$    0.75    0.789    0.9
   $P:12, N_r:7, R_{max}:0,2$    0.775   0.856   0.978
   $P:18, N_r:5, R_{max}:0,7$     0.7    0.667   0.744

  : The NN, FT and ST scores for some runs of the edgeLBP on the Artifacts dataset. The $*$ in the fourth row means that in these settings we 