# A Machine Vision-Based Algorithm for Color Classification of Recycled Wool Fabrics
## 4 Discussion  
When the whole set of 200 fabrics was considered, the average value for the parameter \(\eta\) was 83.2% with a standard deviation of 7.21. The mean value obtained for \(\gamma\) was 93.34%, thus proving that the system achieved good classification results for the given case study.  
In fact, considering the performance evaluation provided in ref. [11], the accuracy of similar methods based on the use of neural networks varied in the range of 57-80% for a dataset composed of a large number of samples and higher number of colors with respect to the dataset used in this study. The method proposed in this work tended to fail more for gray and black color families, where the reliability of the system was approximately 70%. These results, where available, were comparable to those obtained in several similar studies, as demonstrated in Table 3. It should be noted that the classification results in ref. [11] referred to color family rather than color class, whereas the best results from those presented are chosen for comparison with ref. [12].  
In ref. [18], the reliability index was slightly better (95%) since it used more accurate spectral data to perform the classification. In ref. [11], better results were obtained for color families that gave less accurate results using the proposed method. For example, the accuracy obtained for the brown color family was approximately 99%, while no data were available for the beige color family. In any case, the average performance was comparable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fabric id.**} & \multirow{2}{*}{**Family**} & **Class Decided by the** & **PNN Classification** & **PNN Classification** & \\  & & **Pool of Pickers** & \((P_{\text{max}(q_{k})}-\text{max}(q_{k})\) & \((P_{\text{max}-1(q_{k})}-\text{max}-1(q_{k})\) & \(\eta\) \\ \hline \(n_{1}\) & Brown & 9 & 9–93.25\% & 10–2.47\% & 93.87\% \\ \(n_{20}\) & Red & 22 & 22–78.84\% & 21–14.43\% & 79.20\% \\ \(n_{35}\) & Blue & 32 & 32–88.36\% & 31–9.12\% & 90.64\% \\ \(n_{130}\) & Green & 34 & 34–67.52\% & 35–33.25\% & 75.83\% \\ \(n_{143}\) & Gray and black & 37 & 36–52.55\% & 37–38.52\% & 45.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: PNN classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Family**} & **Computer** & **Deep** & \\  & **Vision-Based** & **Learning-Based** & **Proposed Method** \\  & **Method**[7] & **Method**[11] & \\ \hline White & 100\% & 91\% & 80.36\% \\ Beige & n.a. & n.a. & 87.48\% \\ Brown & 98.81\% & 68\% & 92.46\% \\ Orange & n.a. & 92\% & 84.68\% \\ Pink & n.a. & n.a. & 89.24\% \\ Red & 100\% & 81\% & 84.25\% \\ Violet & 100\% & 95\% & 79.36\% \\ Blue & 94.57\% & 86\% & 89.64\% \\ Green & 97.44\% & 76\% & 75.33\% \\ Gray and black & 94.64–100\% & 95\% & 69.32\% \\ Average value & 97.9\% & 86\% & 83.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the proposed method and other methods.  
The scores obtained in ref. [11] were comparable with those obtained in this work for color classes blue, orange, and red with a precision of 79%, 90%, and 81%, in the previous work, respectively, against 90%, 85%, and 85% for the present work, respectively. It is important to remark that the main principles behind the two compared classification systems are completely different, as is true for the datasets. Furthermore, the two mentioned studies in the literature made use of more complex networks requiring more computational effort, while the proposed method employed a simple classificatory with a less restricted platform. It should also be considered that, in the present work, the accuracy is incremented by the choice of lowering the classification penalization for classification in a \"similar\" class.