# Image Representation Using Stacked Colour Histogram
## 4 Experimental Evaluation
### Image Retrieval
Additionally, our method SCH remarkably represents textures and shows high performances in classifying Outtext (i.e., SCH yields 64.24%, which far outperforms CMTH with 42.34% for DAC). We conducted another evaluation using the KNN classifier on all datasets. The mean accuracies when \(k=1\), 96%, 31.80%, 77.63% were recorded for Coil100, Corel10K, and Outtext datasets, respectively. When \(k=3\), 95%, 31.35%, 77.33% were recorded for Coil100, Corel10K, and Outtext datasets, respectively. When the value of \(k=5\), 94%, 34.03%, 76.04% were recorded for Coil100, Corel10K, and Outtext datasets, respectively. Lastly, using \(k=9\) recorded 94%, 31.93%, 77.19% for Coil100, Corel10K, and Outtext datasets, respectively. Table 6 clearly shows the obtained mean accuracies of the KNN classifier, with different values of \(k\), on all datasets. The results obtained were compared with texton-based features in [29]. The accuracy results of our SCH method denote better performance in representing heterogeneous images. Our method performs relatively well for texture images. Table 7 clearly shows the results of SCH together with that of the textons implemented by [PERSON] et al. using different values of k (KNN's parameter). We Present the Results of the IMAGE Retrieval Below  
The SCH is primarily proposed for indexing images for CBIR or QBIC. We evaluated proposed descriptor in a conventional CBIR task. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**k-Value** & **Coil100** & **Corel10K** & **Outtext** \\ \hline k = 1 & 0.96 & 0.31 & 0.77 \\ k = 3 & 0.95 & 0.31 & 0.77 \\ k = 5 & 0.94 & 0.34 & 0.76 \\ k = 9 & 0.94 & 0.31 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean accuracy for KNN classifier with different values of k on three datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Outtext** & **Corel10K** \\ \hline \multirow{8}{*}{k = 1} & TCM & 72.49\% \(\pm\) 2.8\% & 16.71\% \(\pm\) 0.1\% \\  & MTH & 51.92\% \(\pm\) 2.3\% & 23.68\% \(\pm\) 0.1\% \\  & CTM & 72.31\% \(\pm\) 2.8\% & 21.22\% \(\pm\) 0.2\% \\  & NRFU & 36.72\% \(\pm\) 1.7\% & 5.38\% \(\pm\) 0.0\% \\  & CMTH & 78.40\% \(\pm\) 3.0\% & 31.76\% \(\pm\) 0.1\% \\  & SCH & 77.63\% \(\pm\) 3.0\% & 31.80\% \(\pm\) 0.2\% \\  & TCM & 76.42\% \(\pm\) 3.0\% & 16.29 \% \(\pm\) 0.1\% \\  & MTH & 52.30\% \(\pm\) 2.4\% & 22.68 \% \(\pm\) 0.2\% \\  & CTM & 70.18\% \(\pm\) 2.8\% & 20.02 \% \(\pm\) 0.2\% \\  & NRFU & 36.89\% \(\pm\) 1.8\% & 5.26 \% \(\pm\) 0.0\% \\  & CMTH & 76.42\% \(\pm\) 3.0\% & 30.89 \% \(\pm\) 0.2\% \\ k = 3 & SCH & 77.33\% \(\pm\) 3.0\% & 31.35\% \(\pm\) 0.2\% \\  & TCM & 68.41 \% \(\pm\) 2.7\% & 17.51 \% \(\pm\) 0.1\% \\  & MTH & 52.68 \% \(\pm\) 2.5\% & 25.16 \% \(\pm\) 0.2\% \\  & CTM & 68.93 \% \(\pm\) 2.9\% & 20.97 \% \(\pm\) 0.2\% \\  & NRFU & 39.49 \% \(\pm\) 2.1\% & 6.00 \% \(\pm\) 0.08\% \\  & CMTH & 74.83 \% \(\pm\) 3.0\% & 33.16 \% \(\pm\) 0.2\% \\ k = 5 & SCH & 76.04\% \(\pm\) 3.0\% & 34.03\% \(\pm\) 0.2\% \\  & TCM & 72.49\% \(\pm\) 2.8\% & 16.71\% \(\pm\) 0.1\% \\  & MTH & 51.92\% \(\pm\) 2.3\% & 23.68\% \(\pm\) 0.1\% \\  & CTM & 72.31\% \(\pm\) 2.8\% & 21.22\% \(\pm\) 0.2\% \\  & NRFU & 36.72\% \(\pm\) 1.7\% & 5.38\% \(\pm\) 0.0\% \\  & CMTH & 78.40\% \(\pm\) 3.0\% & 31.76\% \(\pm\) 0.1\% \\ k = 9 & SCH & 77.19\% \(\pm\) 3.0\% & 31.93\% \(\pm\) 0.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of SCH together with texton-based features using different values of k (KNN’s parameter). Figure 3 presents the average precision values of five window sizes and the five retrieved values (6, 12, 18, 24 and 30). The experiment determined which window size and the number of recurrent transformation will be adequate for SCH. From Figure 4, we can see that different datasets recorded different precision patterns. COIL-100 precision values increased with increasing window size. However, Batik, Corel-10K and Outext did not record such a pattern, especially a Batik depicting alternating precision with increasing window size.