# A New Smoke Segmentation Method Based on Improved Adaptive Density Peak Clustering
## 1 Introduction
This method can effectively segment infrared ship images, but it needs to manually determine the number of clusters, fuzzy factors, and other parameters. [PERSON] et al. (2019) used a combination of SLIC and DBSCAN to achieve the segmentation of fire areas, combined with convolutional neural networks to identify fires. K-means and FCM are classic clustering algorithms, but they have many adjustment parameters and are prone to local optimal solutions. [PERSON] et al. (2019) propose an automatic segmentation of foveal avascular zone (FAZ) based on adaptive watershed algorithm in retinal optical coherence tomography angiography images. They analyze the relationship between the \"dams\" length and the maximum inscribed circle radius of FAZ, and propose an adaptive watershed algorithm to solve the problem of \"over-segmentation\". [PERSON] et al. (2019) propose a lorenz curve-based entropy thresholding on circular histogram. They introduce the lorenz curve into circular histogram and use the entropy threshold of linearized circular histogram to select the best threshold. Density Peak Clustering (DPC) ([PERSON] et al., 2019) is a clustering algorithm that does not require iteration and it is widely used in image segmentation. [PERSON] et al. (2019) propose multi-center density peak clustering (McDPC). McDPC uses the assumption that the potential clustering centers are far apart to obtain representative data points and then combines the representative data points after classification. McDPC is used for image segmentation and facial recognition to obtain better experimental results. [PERSON] et al. (2019) propose a modified PageRank algorithm to compute the local density for each data point. In image segmentation, this algorithm achieves better experimental results than DPC. [PERSON] et al. (2019) propose a linear fitting density peaks clustering algorithm for image segmentation (LDPC). They use a linear fitting method to choose clustering centers automatically; LDPC has better segmentation performance compared to DPC and K-means. [PERSON] et al. (2019) propose medical image segmentation using fruit fly optimization and density peaks clustering. They combine DPC with the fruit fly optimization algorithm, which can automatically determine the clustering centers and truncation distance; in medical image segmentation, this algorithm has the advantages of fast convergence speed and good robustness. [PERSON] et al. (2019) propose peak-map-based fast density peak clustering for image segmentation (PGDPC). They use the KNN distance to calculate the local density and use a peak-map-based allocation strategy,which can quickly and accurately segment the image, but the number of DPC clustering centers needs to be manually selected. DPC has the advantages of fewer adjustment parameters, no need for an iterative solution, etc. However, the local density defined by the DPC algorithm does not consider the structural differences of high-dimensional data, and sometimes cannot truly reflect the local density of sample points; the truncation distance \(d_{c}\) of DPC needs to be manually selected. If the truncation distance is too large or too small, the clustering effect will be worse; DPC needs to manually select the clustering center through the decision diagram. Many scholars have made improvements to the shortcomings of DPC [29; 30; 31; 32; 33]. In order to improve the segmentation accuracy of smoke images and find out the smoke regions accurately, a smoke segmentation method based on improved adaptive density peak clustering is proposed. TSDPC redefines the local density of the sample point through the density calculation equation and expands the density difference between the target area and the background. TSDPC obtains the optimal double truncation distance through information entropy theory. Then, TSDPC uses trigonometric functions to determine the number of clustering centers in the decision diagram.