# Complex Color Space Segmentation to Classify Objects in Urban Environments
## 2 Related Works  
Labeling segmented areas require high computational resources to recognize objects during the human\(-\)machine interaction. Image segmentation is often based on the graphs theory and grouping algorithms. In [10], the authors propose a general scheme of segmentation of scenes based on the spectral grouping algorithm for normalized cuts, fusing geometric and color information on a working frame with no parameters. The study in [11] presents a segmentation scheme to combine color and depth information. Under this scheme, segmentation happens in 3 steps. The study by [PERSON] [12] identifies the types of rocks using images of rocky settings. Segmentation has been extended to video; for instance, the authors of [13] have developed a method to combine the appearance of an object with the temporal consistency between frames. Using the features of a normalized-color histogram and CNN features, the GrabCut algorithm is applied to different frame boundaries to segment the object in the background. When detecting objects in motion, the background is obtained using videos taken from a static camera. The study presented in [14] suggests the option of a detection and segmentation method based on consecutive stereo images that process dynamic objects found in an urban environment. This is a pixel-by-pixel approach applied to the KITTI dataset [15], and the frame boundaries are generated bearing in mind color and difference data for each moving object. Unlike object-detection strategies, object recognition focuses on the objectives of the image and provides a specific class for each one [16]. As the objective is better adjusted to the frame boundary, the classification results become more reliable without a background. The fast development of smart vehicles makes object detection and recognition essential in self-driving [1]. In addition, road sign detection provides key information for safe navigation. Often, road detection is based on standard low-profile features used to process the image and isolate the borders. In [17], a real-time two-stage YOLOv2-based road-sign detection system is used. In the first stage, the YOLOv2 detection frame is modified to adapt it to the road-sign detection task and predicts boundary frames, class, and reliability of road signage. In the second stage, an invariant light road-sign transformation network (RM-Net) reclassifies the samples with low accuracy to increase accuracy. The CNN architectures used for segmentation purposes are usually of three kinds, fully convolutional networks (FCN) [18], coder-decoder networks [19] and \"atrous-convolutional\" networks [20]. The authors in [21] introduce the Mask R-CNN method, an extension of the Faster R-CNN method [22] to segment images instead of just detecting boundary frames. There are also some approaches whereby Deep Neural Networks are modified, for instance, semantic-aware segmentation [23] to use semantic segmentation and instance segmentation. Recent strategies propose general DWT and IDWT layers to various wavelets and design wavelet integrated CNNs (WaveCNets) for image classification using ImageNet and ImageNet-C, achieving an accuracy of 78.51% [24]. Moreover, a new architecture (VOLO) implements a novel outlook attention operation that dynamically conducts the local feature aggregation mechanism in a sliding window across the input image. This approach uses transformers and CNNs to complement their model and achieves 87.1% using ImageNet-1k [25]. Another natural color image approach is described in [26]. In this approach, the image is split into patches that feed the embedding module to expand the feature dimensions used for image classification [26]. This method achieves 83.9% in the Top-1 accuracy rate.