# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 2 Related Work
### Gradient-Based Algorithms  
Algorithms in this category extract superpixels by either gradient ascent or gradient descent. The early gradient-based method, known as watershed [31], obtains local minima and the similarity between neighboring pixels to segment images; however, it is sensitive to noise, and the shapes of superpixels are very irregular. In the last decade, gradually refining the initial segmentation regions has become the mainstream.  
The Turbopixels method, proposed by [PERSON] et al. [32], aims to generate regular superpixels using level set based geometric flow evolution. This method starts with seed points and then iteratively dilates these seeds to generate highly uniform lattice-like superpixels. Although the superpixels are very compact, their adherence to boundaries is low, and this method is also slow, due to stability and efficiency issues of the level set. [PERSON] et al. [33] have proposed a structure-sensitive superpixel technique using the geodesic distance. In this method, the geodesic distance obtained by geometric flows between pixels is adopted to guide clustering, which improves the segmentation accuracy. Although optimizing an energy function consisting of color homogeneity and structure density can adapt the size and number of superpixels to the image content, the computational cost is very high.  
Simple linear iterative clustering (SLIC), proposed by [PERSON] et al. [12], is the most popular gradient-based segmentation algorithm. The scope of K-means clustering is first limited, which can reduce the time complexity to linear in each iteration. Although this method has high efficiency in each iteration, the cluster center requires multiple iterations to adhere to the image boundary. To generate superpixels in only one iteration, the author then proposed simple non-iterative clustering (SNIC) [34], using a priority queue and a real-time central point update strategy. However, the time cost of SNIC is not significantly reduced, especially in high-resolution images, and the boundary adherence capability is insufficient in complex regions. [PERSON] et al. [18] utilized a multi-feature weighted similarity measure function to increase the segmentation accuracy.  
The above method is not sensitive to image content. To produce content-sensitive atomic regions, [PERSON] et al. [10] have proposed manifold SLIC by mapping the input image to a two-dimensional manifold \(M\subset R^{5}\), which can be used to measure the content density. Thus, the search range of each superpixel center can be adapted to the image content, but additional superpixels cannot be added in dense regions to improve boundary adherence. [PERSON] et al. [15] adopted hierarchical multi-level SLIC (Li-SLIC) to adaptively segment texture-sparse and texture-dense regions. Li-SLIC decides whether to perform the segmentation again by evaluating the color standard deviation of each superpixel. This approach is less robust and cannot be used in dense and sparse mixed regions. [PERSON] et al. [11] have developed a method in which the seed scattering conforms to the image complexity, according to the principle of image entropy equalization. However, their method relies heavily on manually preset values and cannot accurately place seed points within each object. At present, few methods can generate superpixels or an initial seed set that exactly matches the image structure.