SLIC超像素算法：高效生成紧凑均匀超级像素

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"SLIC超像素算法是一种在计算机视觉领域广泛应用的超像素分割方法，由Radhakrishna Achanta等人在2010年提出。SLIC代表“Simple Linear Iterative Clustering”（简单线性迭代聚类），它结合了颜色和空间信息，生成具有低计算复杂度、规则且紧凑的超像素。这种方法在保持高效率的同时，能提供与现有先进方法相当甚至更好的分割质量。" SLIC超像素算法的核心思想是将图像中的像素在五维空间（RGB颜色空间加上两个空间坐标）中进行聚类。这个五维空间包括三个颜色维度（红、绿、蓝）和两个位置维度（x、y）。算法的主要优点在于用户只需指定期望的超像素数量，然后算法会自动进行划分。算法流程大致如下： 1. 初始化：首先，将图像中的每个像素视为一个点，根据RGB值和空间位置创建五维向量。 2. 聚类：采用K-means聚类方法，将这些点分配到预设数量的聚类中心。SLIC改进了传统的K-means，通过引入距离度量，考虑了颜色和空间邻近性的结合，使得生成的超像素更加连续和紧凑。 3. 更新：计算每个聚类的新中心，通常采用加权平均法，权重可以考虑像素的面积或亮度等。 4. 迭代：重复步骤2和3，直到满足停止条件，如达到最大迭代次数或超像素质量不再显著提升。 SLIC的优势在于其高效性和灵活性。它的计算复杂度相对较低，适用于实时或资源有限的环境。此外，由于超像素边界接近图像的自然边缘，因此在后续的图像分析和处理任务中，如物体检测、语义分割等，SLIC超像素可以提供良好的基础。与其他方法比较，SLIC在保持相似或更优的边界召回率和欠分割误差的同时，计算成本更低。实验表明，SLIC在对比现有的超像素算法时，在两个任务中展示了优越性，这进一步证明了其在计算机视觉领域的实用性。 SLIC超像素算法是图像分割领域的一个重要工具，它的简单性、高效性和优良的分割质量使其成为许多应用的首选方法。通过合理调整参数，可以适应各种不同的图像和场景需求，为后续的计算机视觉任务提供高质量的预处理结果。

EPFL Technical Report 149300 3

Table 1. Comparison of state of the art superpixel segmentation algorithms. N is the

number of pixels in the image. GS04 and QS09 do not oﬀer explicit control of the

number of superpixels. SL08 complexity given in this table does not take into account

the complexity of the boundary map computation. GS04 is O (N logN) complex but is

comparable in speed to SLIC for images less than 0.5 million pixels while TP09 is also

O (N ) complex but is 10 times slower than SLIC for 481 × 321 pixel images. In the case

of QS09, d is a small constant (refer to [10] for details). The number of parameters

listed in the table is the minimum required for typical usage.

Graph-based Gradient-ascent-based

Properties GS04 NC05 SL08 WS91 MS02 TP09 QS09 SLIC

Superpixel no. ctrl. No Yes Yes No No Yes No Yes

Compactness ctrl. No Yes Yes No No Yes No Yes

Complexity O(.) NlogN N

3/2

logN NlogN N

N dN

Parameters 2 1 3 1 3 1 2 1

2.1 Graph-based algorithms

In graph based algorithms, each pixel is treated as a node in a graph, and

edge weight between two nodes are set proportional to the similarity between

the pixels. Superpixel segments are extracted by eﬀectively minimizing a cost

function deﬁned on the graph.

The Normalized cuts algorithm [9], recursively partitions a given graph using

contour and texture cues, thereby globally minimizing a cost function deﬁned on

the edges at the partition boundaries. It is the basis of the superpixel segmenta-

tion scheme of [1] and [6] (NC05). NC05 has a complexity of O(N

) [12], where

N is the number of pixels. There have been attempts to speed up the algorithm

[13], but it remains computationally expensive for large images. The superpixels

from NC05 have been used in body model estimation [6] and skeletonization [5].

Fezenszwalb and Huttenlocher [8] (GS04) present another graph-based seg-

mentation scheme that has been used to generate superpixels. This algorithm

performs an agglomerative clustering of pixel nodes on a graph, such that each

segment, or superpixel, is the shortest spanning tree of the constituent pixels.

GS04 has been used for depth estimation [2]. It is O(NlogN) complex and is

quite fast in practice as compared to NC05. However, unlike NC05, it does not

oﬀer an explicit control on the number of superpixels or their compactness.

A superpixel lattice is generated by [14] (SL08) by ﬁnding optimal vertical

(horizontal) seams/paths that cut the image, within vertical (horizontal) strips

of pixels, using graph cuts on strips of the image. While SL08 allows control of

the size, number, and compactness of the superpixels, the quality and speed of

the output strongly depend on pre-computed boundary maps.

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现有两张相同大小的图像A与B，要求：对A进行超像素分割，记录A的超像素块范围， 直接将记录的每块超像素块范围应用在B上，最后显示A,B的超像素分割图像，给出pytorch代码

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现有两张相同大小的图像A与B，要求：对A进行超像素分割，记录A的超像素块范围，直接将记录的每块超像素块范围应用在B上，最后显示A,B的超像素分割图像，给出pytorch代码