10 classes considered in the Artifacts dataset.[]{data-label="fig:GRAV_col"}](pic/cptVI.png "fig:"){width="2cm"}   ![Representatives of the 10 classes considered in the Artifacts dataset.[]{data-label="fig:GRAV_col"}](pic/cptVII.png "fig:"){width="2cm"}   ![Representatives of the 10 classes considered in the Artifacts dataset.[]{data-label="fig:GRAV_col"}](pic/cptVIII.png "fig:"){width="2cm"}   ![Representatives of the 10 classes considered in the Artifacts dataset.[]{data-label="fig:GRAV_col"}](pic/cptIX.png "fig:"){width="2cm"}   ![Representatives of the 10 classes considered in the Artifacts dataset.[]{data-label="fig:GRAV_col"}](pic/cptXb.png "fig:"){width="2cm"}
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The edgeLBP algorithm is used to perform colorimetric pattern retrieval on the CCP and Artifact datasets, separately.

Evaluation measures {#ss:eval_mes}
-------------------

The evaluation tests have been performed using a number of classical information retrieval measures, namely the Nearest Neighbor, First Tier, Second Tier, Discounted Cumulative Gain, e-measure, Precision-Recall plot, confusion matrices and tier images.

#### Nearest Neighbor, First Tier, Second Tier {#nearest-neighbor-first-tier-second-tier .unnumbered}

These measures aim at checking the fraction of models in the query’s class also appearing within the top $k$ retrievals. In detail, for a class with $|C|$ members, $k=1$ for the Nearest Neighbor (NN), $k =|C|-1$ for the first tier (FT), and $k = 2(|C| - 1)$ for the second tier (ST). Note that all these values range from 0 to 1.

#### Discounted cumulative gain {#discounted-cumulative-gain .unnumbered}

The Discounted Cumulative Gain (DCG) is an enhanced variation of the Cumulative 