the CCP dataset. Top: the NN, FT, ST, e-measure, mAP and nDGC evaluation measures. Middle: the Precision-Recall curves. Bottom: the confusion matrix and tier image of the edgeLBP and the meshLBP runs.[]{data-label="fig:ccp_res"}](pic/ccp_cm_meshLBP.png "fig:"){width="3cm"}   ![Performance evaluation on the CCP dataset. Top: the NN, FT, ST, e-measure, mAP and nDGC evaluation measures. Middle: the Precision-Recall curves. Bottom: the confusion matrix and tier image of the edgeLBP and the meshLBP runs.[]{data-label="fig:ccp_res"}](pic/ccp_ti_meshLBP.png "fig:"){width="3cm"}
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The classification and retrieval results obtained over this dataset are very promising and highlight how the edgeLBP encoding captures the pattern distribution over the surface. The edgeLBP overcome simple histogram-based descriptions that, in practice, measure the percentage of color distribution without any control around vertices and also the meshLBP description that bases the ring definition on mesh elements. The positive edgeLBP perfomance is confirmed in the recent SHREC’18 track for gray color patterns [@shrec18].

Artifacts dataset {#artifacts-dataset .unnumbered}
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This dataset is challenging because of the quality of the original fragments, as their colorimetric patterns are degraded and damaged. Table \[tb:GRA\_res\] reports the NN, FT and ST evaluations for different parameter settings of the edgeLBP. Confusion matrices for the two best radius values are reported in Figure 