# Image Representation Using Stacked Colour Histogram
## 4 Experimental Evaluation
### Image Retrieval  
In this section, the proposed descriptor is evaluated in a classical CBIR task. Precision and recall metrics are used to evaluate the proposed descriptor's performance in the CBIR task. Equations (3) and (4) present the formulae for the precision and recall metrics, respectively. \[Precision=\frac{number\ of\ retrieved\ relevant\ images}{Number\ of\ retrieved\ images} \tag{3}\]  
\[Recall=\frac{number\ of\ retrieved\ relevant\ images}{Number\ of\ relevant\ images\ in\ the\ dataset} \tag{4}\]  
We first present the results of the machine learning approach used for the experiment. Table 1 provides accurate results of the DCA classifier on all datasets. The window size \(11\times 11\) with the recurrent transformation of 5 provided the highest accuracy of 91% for Coill100. All window sizes with the recurrent transformation of 5 gave 36% accuracy as highest for Corel10K. The window sizes \(7\times 7\) and \(9\times 9\) with the recurrent transformation of 5 produced 67% accuracy for the Outext dataset. The mean accuracy of 87.64%, 32.72%, and 64.24% was recorded for Coill100, Corel10K and Outext datasets, respectively. Table 2 displays the accuracy results of the SVM classifier on all datasets. The window size \(11\times 11\) with the recurrent transformations of 15, 20 and 25 yielded the highest accuracy of 97% for Coil100. The window size \(11\times 11\) with the recurrent transformation of 5 produced an accuracy of 43% as the highest for Corel10K. The window sizes \(3\times 3\), \(5\times 5\) and \(7\times 7\) with the recurrent transformations of 15, 20 and 25 produced 78% accuracy for  
Figure 2: Sample images from each dataset. (**a**) Outex, (**b**) Corel10K, (**c**) Coil100 and (**d**) Batik. the Outext dataset. Mean accuracies of 95.16%, 40.96%, and 77.08% were recorded for the Coil100, Core110K and Outext datasets, respectively. Table 3 presents accuracies obtained by applying the NB classifier on all datasets. Most 9 \(\times\) 9 and 11 \(\times\) 11 window sizes with the recurrent transformations of 15, 20 and 25 produced the highest accuracy of 92% for Coil100. The window sizes 5 \(\times\) 5 and 11 \(\times\) 11 with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Window Size – Recurrent Number** & **Coil100** & **Core110K** & **Outext** \\ \hline \(3\times 3-5\) & 0.85 & 0.34 & 0.64 \\ \(3\times 3-10\) & 0.85 & 0.34 & 0.64 \\ \(3\times 3-15\) & 0.85  
the recurrent transformations of 15 and 25 produced an accuracy of 36% as the highest for Corel10K. The window size 3 \(\times\) 3 with the recurrent transformations of 15 and 20 produced 64%, whereas window size 5 \(\times\) 5 with the recurrent transformation of 5 produced the same accuracy for the Outext dataset. The mean accuracies for Coil100, Corel10K, and Outext were 89.96%, 34.28%, and 61.08%, respectively. Table 4 shows the accuracy outcomes by applying the DT classifier on all datasets. The window size 11 \(\times\) 11 with the recurrent transformations of 15 and 25 produced the highest accuracy of 92% for Coil100. The window sizes 5 \(\times\) 5 and 11 \(\times\) 11 with the recurrent transformations of 15 and 25 produced an accuracy of 24% as the highest for Corel10K. The window size 5 \(\times\) 5 with the recurrent transformations of 20 and 25 produced 79% accuracy for the Outext dataset. Mean accuracies of 88.96%, 22.96%, and 76.32% were recorded for the Coil100, Corel10K, and Outext datasets. From Tables 1-4, generally window size 11 \(\times\) 11 with the recurrent transformations of 15, 20 and 25 produced good results for the four different classifiers. [PERSON] et al. [29] in their work _\"Image representation using complete multi-text histogram\"_, compared their results with the state-of-the art texture feature extraction techniques for indexing images or representing images. The Texton Co-occurrence Matrix (TCM), Multi-Texton Histogram (MTH), Complete Texton Matrix (CTM), Complete Multi-Texton Histogram (CMTH), and Noise Resistant Fundamental Units of Complete Texton Matrix (NRFUCTM) were compared in the paper.