# Image Representation Using Stacked Colour Histogram
## 4 Experimental Evaluation
### Image Retrieval
Table 5 presents the results of accuracy obtained by [PERSON] et al. The result compared for four different classifiers, namely Discriminant Analysis Classifier (DAC), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) for well-established texton-based feature extraction techniques. The texton-based features presented by [PERSON] et al. had worse performance in representing heterogeneous images. Our proposed SCH outper  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Window Size – Recurrent Number** & **Coil100** & **Corel10K** & **Outex** \\ \hline \(3\times 3-5\) & 0.87 & 0.33 & 0.61 \\ \(3\times 3-10\) & 0.88 & 0.34 & 0.63 \\ \(3\times 3-15\) & 0.89 & 0.33 & 0.64 \\ \(3\times 3-20\) & 0.88 & 0.34 & 0.64 \\ \(3\times 3-20\) & 0.88 & 0.34 & 0.63 \\ \(5\times 5-5\) & 0.89 & 0.  
form the textons (i.e., CMTH yields 31.76% with Corel10K. However, our method SCH outperform with 32.72% for DAC). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Window Size – Recurrent Number** & **Coil100** & **Corel10K** & **Outex** \\ \hline \(3\times 3-5\) & 0.85 & 0.23 & 0.72 \\ \(3\times 3-10\) & 0.86 & 0.22 & 0.73 \\ \(3\times 3-15\) & 0.86 & 0.23 & 0.77 \\ \(3\times 3-20\) & 0.87 & 0.22 & 0.76 \\ \(3\times 3-25\) & 0.88 & 0.23 & 0.76 \\ \(5\times 5-5\) & 0.87 & 0.23 & 0.75 \\ \(5\times 5-10\) & 0.89 & 0.23 & 0.77 \\ \(5\times 5-15\) & 0.88 & 0.23 & 0.78 \\ \(5\times 5-20\) & 0.89 & 0.22 & 0.79 \\ \(5\times 5-25\) & 0.89 & 0.23 & 0.79 \\ \(7\times 7-5\) & 0.88 & 0.23 & 0.75 \\ \(7\times 7-10\) & 0.9 & 0.23 & 0.77 \\ \(7\times 7-15\) & 0.89 & 0.23 & 0.77 \\ \(7\times 7-20\) & 0.89 & 0.23 & 0.77 \\ \(7\times 7-25\) & 0.9 & 0.22 & 0.78 \\ \(9\times 9-5\) & 0.89 & 0.22 & 0.75 \\ \(9\times 9-10\) & 0.9 & 0.23 & 0.77 \\ \(9\times 9-15\) & 0.89 & 0.24 & 0.77 \\ \(9\times 9-20\) & 0.91 & 0.23 & 0.77 \\ \(9\times 9-25\) & 0.91 & 0.24 & 0.77 \\ \(11\times 11-5\) & 0.89 & 0.23 & 0.75 \\ \(11\times 11-10\) & 0.91 & 0.23 & 0.76 \\ \(11\times 11-15\) & 0.92 & 0.24 & 0.76 \\ \(11\times 11-20\) & 0.9 & 0.23 & 0.76 \\ \(11\times 11-25\) & 0.92 & 0.24 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Presents accuracies for the various window sizes and transformations for the three datasets using DT. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Outerx** & **Corel10K** \\ \hline \multirow{8}{*}{DAC} & TCM & 41.96 \% \(\pm\) 2.5\% & 16.71\% \(\pm\) 0.1\% \\  & MTH & 33.85 \% \(\pm\) 2.4\% & 23.68\% \(\pm\) 0.1\% \\  & CTM & 33.65 \% \(\pm\) 1.8\% & 21.22\% \(\pm\) 0.2\% \\  & NRFU & 39.83 \% \(\pm\) 2.5\% & 5.38\% \(\pm\) 0.0\% \\  & CMTH & 42.34 \% \(\pm\) 2.6\% & 31.76\% \(\pm\) 0.1\% \\  & SCH & 64.24\% \(\pm\) 2.5\% & 32.72\% \(\pm\) 0.1\% \\  & TCM & 42.95 \% \(\pm\) 3.0\% & 20.58 \% \(\pm\) 0.1\% \\  & MTH & 43.51 \% \(\pm\) 2.6\% & 33.02 \% \(\pm\) 0.1\% \\  & CTM & 27.43 \% \(\pm\) 1.3\% & 21.15 \% \(\pm\) 0.2\% \\  & NRFU & 31.51 \% \(\pm\) 1.9\% & 11.69 \% \(\pm\) 0.1\% \\  & CMTH & 51.01 \% \(\pm\) 1.6\% & 40.61 \% \(\pm\) 0.2\% \\   SVM & SCH & 77.08\% \(\pm\) 0.1\% & 40.96\% \(\pm\) 0.2\% \\  & TCM & 37.24 \% \(\pm\) 1.7\% & 23.62 \% \(\pm\) 0.2\% \\  & MTH & 25.54 \% \(\pm\) 1.1\% & 24.68 \% \(\pm\) 0.2\% \\  & CTM & 34.97 \% \(\pm\) 1.5\% & 23.06 \% \(\pm\) 0.2\% \\  & NRFU & 41.01 \% \(\pm\) 1.5\% & 22.71 \% \(\pm\) 0.2\% \\  & CMTH & 38.71 \% \(\pm\) 1.5\% & 34.22 \% \(\pm\) 0.2\% \\ NB & SCH & 61.08\% \(\pm\) 0.1\% & 34.28\% \(\pm\) 0.2\% \\  & TCM & 64.63 \% \(\pm\) 2.5 & 18.33 \% \(\pm\) 0.1\% \\  & MTH & 47.70 \% \(\pm\) 2.3\% & 17.23 \% \(\pm\) 0.1\% \\  & CTM & 67.38 \% \(\pm\) 2.7\% & 19.69 \% \(\pm\) 0.1\% \\  & NRFU & 60.81 \% \(\pm\) 2.6\% & 15.96 \% \(\pm\) 0.1\% \\  & CMTH & 64.45 \% \(\pm\) 2.8\% & 22.94 \% \(\pm\) 0.1\% \\ DT & SCH & 76.32 \% \(\pm\) 0.1\% & 22.96\% \(\pm\) 0.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained by classifying images from each dataset using DAC, SVM, NB and DT.