# Complex Color Space Segmentation to Classify Objects in Urban Environments
## 6 Conclusions  
This study shows a new approach to image segmentation to identify objects in structured outdoor spaces. The approach extracts representative features based on combining algebra for complex numbers on planes \(a\) and \(b\) on the CIELab color space. The complex image makes it possible to develop and implement a multi-threshold segmentation algorithm. The methodology follows a typical automatic learning technique. The required features to input the classifier are chosen from specific areas on the segmented image. Despite light and overcrowding issues in outdoor environments, the number of classes and images used in the training and validation phases of the model are enough to execute the identification of objects. The multi-threshold segmentation algorithm produces different execution time lapses depending on the image features to be processed. This is also dependent on the computing power available. In addition, the different sets of images used for CNN training and validation are created using random conditions. The execution time results for the multi-threshold segmentation algorithm depend on the size and features of the image. Thus far, this approach cannot be used in real-time conditions that require execution speeds of milliseconds. However, a dispersal strategy to select different areas on the scene could provide lighter techniques for classification purposes. Given the modular nature of the methodology, modifications to increase hardware performance are possible. The VGG-16 network responds well to conditions such as those in this study, showing a uniform and flexible architecture; however, better accuracy results were achieved using the ResNet-150 network. Execution times for classification purposes are affected by the various phases in the methodology and the different features of the images from the databases. Hence, the decision to train the CNN architecture using the Cityscape dataset and validate it using the CamVid dataset shows similar outdoor and urban environments. Finally, this study has focused on a less computationally intensive alternative to conducting color segmentation and object detection tasks, with the flexibility of adapting to different hardware architectures and scenarios. Conceptualization, D.-L.A.-O. and M.-A.I.-M.; methodology, D.-A.R.-M. and D.-L.A.-O. ; software, J.-J.-C.-C. and M.-A.I.-M.; validation, J.-J.-C.-C. and D.-A.R.-M.; formal analysis, J.-J.-C.-C., D.-L.A.-O. and M.-A.I.-M.; investigation, J.-J.-C.-C. and D.-A.R.-M.; data curation, J.-J.-C.-C. and D.-L.A.-O. ; writing--original draft preparation, J.-J.-C.-C.; writing--review and editing, D.-L.A.-O. and M.-A.I.-M.; visualization, D.-L.A.-O., D.-A.R.-M. and M.-A.I.-M.; project administration, D.-L.A.-O. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Classification Learner**} & \multicolumn{3}{c}{**Patches Sizes (in Pixels)**} \\ \cline{2-4}  & \multicolumn{2}{c}{\(96\times 96\)} & \(128\times 128\) & \(224\times 224\) \\ \hline Quadratic SVM & 78.10\% & 78.30\% & 78.90\% \\ \hline Cubic SVM & 78.50\% & 79.10\% & 80.10\% \\ \hline Fine Gaussian SVM & 67.30\% & 70.20\% & 74.10\% \\ \hline Medium Gaussian SVM & 77.80\% & 77.80\% & 77.40\% \\ \hline Bagged Trees & 79.20\% & 79.40\% & 79.80\% \\ \hline Narrow Neural Network & 72.90\% & 74.20\% & 76.20\% \\ \hline Medium Neural Network & 74.60\% & 76.60\% & 78.70\% \\ \hline Bilayered Neural Network & 72.90\% & 74.20\% & 77.00\% \\ \hline Trilayered Neural Network & 73.70\% & 74.60\% & 76.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification result using machine learning approaches. **Funding:** This study was conducted as part of the doctoral studies of [PERSON], funded through scholarship number 2021-000018-02 NACF-07210, awarded by CONACYT. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors are grateful to the University of Guanajuato.