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首页SLICSuperpixels:高效生成高质量图像超像素的方法
SLICSuperpixels:高效生成高质量图像超像素的方法
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更新于2024-07-17
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SLIC-superpixels是计算机视觉领域中一项关键的技术,它起源于一篇由Radhakrishna Achanta等人在2012年发表的开创性论文。这篇论文标志着超像素(Superpixels)这一概念在图像分割中的广泛应用达到了新的高度。超像素是一种将像素集合成具有相似颜色和纹理特征的区域的技术,这在许多任务中,如物体识别、图像分类和图像理解中发挥着重要作用。 该算法的核心在于其创新的SLIC (Simple Linear Iterative Clustering) 方法,它将像素的五维空间(包括RGB颜色值和两个空间坐标)考虑在内,实现了高效且紧凑的超像素生成。SLIC的主要优势在于其参数设置简单,只需要指定所需的超级像素数量,就能快速得到结果。这种效率使得SLIC成为实际应用中的一种理想选择。 实验结果显示,尽管SLIC算法在计算成本上相对较低,但其分割质量却能与当时最先进的四种方法相媲美,甚至在边界召回率和欠分割误差等评估指标上有所超越。这证明了SLIC在保持高精度的同时,还具备了出色的性能和实用性。 此外,论文作者还展示了他们的超像素方法相对于现有技术在两个具体任务中的优势,这可能包括对象检测、图像检索或者图像拼接等场景,其中SLIC能够提供更均匀、更连续的区域划分,有助于提升任务的执行效果和最终结果的视觉一致性。 SLIC-superpixels是计算机视觉领域中的一项里程碑,它通过结合颜色、空间信息的高效聚类,为图像分割带来了革命性的改进,使得在处理大规模图像时既能保证速度又能保证精度,对于提升视觉应用的性能具有深远的影响。
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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
N
2
logN NlogN N
2
N dN
2
N
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
3
2
) [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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