# Images Segmentation Based on Cutting the Graph into Communities
## 2 Related Works  
Communities can stand out using a variety of algorithms. Direct application of fast and greedy method [12] and label propagation algorithms [13] is computationally intensive. Therefore, additional transformations are applied. Image partitions into superpixels [14] are one of these transformations. The superpixel is a small image segment [15]. There are several approaches to breaking an image into superpixels [16; 17; 18; 19]. The accuracy of this approach is limited by the superpixels size. Another way to reduce the complexity of community-based segmentation on a graph is based on reducing the amount of information. The threshold principle for determining the graph edge weight reduces the image graph connectivity [20]. This approach segments monochrome images based on highlighting communities on a graph. However, this algorithm is very sensitive to threshold selection. The threshold value is selected manually for each image. The search for communities in the graph can be used in the post-processing step of the segmented image [21]. The graph is mapped to a segmented image. Some segments are further combined to optimize graph partitioning [21; 22]. This approach is effective for images with a large number of small segments [23]. The algorithm for segmentation of the original image based on communities on graphs was proposed in reference [24]. However, this algorithm includes some simplifications. The algorithm considers only the edges for the nearest neighbors. The algorithm has a low speed. Segmentation based on the graph-theoretic approach and the search for communities can only be applied to a part of the image. The number of pixels decreases significantly after the background is removed. This transformation allows the construction of a fully connected graph based on superpixels [25]. After that, the authors of this work performed clustering of the graph based on geometric features. The third step was to refine the boundaries. Feature vectors can be used to cluster graphs corresponding to the image [26]. Feature vectors are constructed from the internal structure of graphs and reflect the internal nodes' connectivity. Feature vectors can be used in various graph clustering algorithms. Algorithms that exist to analyze social networks can be used to detect communities on graphs derived from the image [27; 28]. In this case, it is necessary to reduce the number of nodes that are performed using superpixels. Subgraph splitting methods based on community allocation produce the best results for artificial objects such as cultural heritage artifacts [29]. The eigenvector of the modularity matrix corresponding to the largest positive eigenvalue, the authors recursively identify multilevel subgroups in the graph. The cluster tree strategy creates a cluster map with a variable level of detail. This approach finds the optimal number of clusters. The main problem with using community search algorithms on graphs for image segmentation is the large amount of computation. One possible solution is to distribute labels [30]. This approach selects a node label to optimize the objective function by considering the available labels in the neighborhood of the node. There are several algorithms for detecting the road in an image using a vanishing point: the dominant segment method [31], the parallel line method [32] and the pixel texture orientation method [33]. The most likely area of the road is assessed by the soft voting scheme [33; 34] or by comparison with the pattern [35] in these methods. These methods provide a good result for flat asphalt roads with a flat edge. For roads with uneven lighting, the result is poor. Noises and obstacles strongly influence segmentation outcomes. These algorithms have high computational complexity and are not applicable in real-time systems. These algorithms are also not applicable in real road conditions in the presence of cars and pedestrians. Road selection algorithms based on image segmentation typically involve two steps.