Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1931-1937.doi: 10.13229/j.cnki.jdxbgxb20171009

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Image segmentation algorithm based on SLIC and fast nearest neighbor region merging

LIU Zhong-min1(),WANG Yang1,LI Zhan-ming1,HU Wen-jin2   

  1. 1. College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China
    2. College of Mathematics and Computer Science,Northwest Minzu University,Lanzhou 730030,China
  • Received:2017-09-27 Online:2018-11-20 Published:2018-12-11

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Abstract:

It is difficult to search the global optimal solution when traditional region adjacency graph is used to describe the data structure. To solve this problem, an image segmentation algorithm based on SLIC and fast nearest neighbor region merging is proposed. This method introduces the nearest neighbor graph to optimize the global search based on the region adjacency graph. First, an image is divided into small regions by SLIC superpixel algorithm, in which the relation between regions is described by the adjacency table structure of the Region Adjacency Graph (RAG) and the Nearest Neighbor Graph (NNG). Then, the value of the dissimilarity function is calculated between each region to be merged with all of its adjacent regions. Finally, the region with the least similarity is merged. The experimental results show that the proposed algorithm can combine the most similar regions, and reduce the complexity of the merging calculation compared with the traditional region merging algorithm, which greatly improves the accuracy of the regional merging.

Key words: information processing technology, image segmentation, simple linear iterative clustering(SLIC), region adjacency graph(RAG), nearest neighbor graph(NNG), region merging

CLC Number: 

  • TP391.4

Fig.1

Segmentation results with different k values"

Fig.2

Segmentation results with different m values"

Fig.3

Schematic diagrams of RAG and NNG"

Fig.4

RAG schematics and conversion to NNG schematics"

Fig.5

Segmentation results"

Table 1

Segmentation evaluation index"

算法 第1幅图像 第2幅图像 第3幅图像
Precision Recall F-score Precision Recall F-score Precision Recall F-score
NCUT 0.4752 0.5621 0.5150 0.4092 0.6561 0.5040 0.5628 0.5426 0.5525
SCOW 0.6193 0.7036 0.6587 0.7969 0.8126 0.8046 0.6927 0.7683 0.7285
SLIC 0.6279 0.7964 0.7021 0.8024 0.7873 0.7947 0.7638 0.7026 0.7319
SLIC+DBSCAN 0.4235 0.6718 0.5195 0.6499 0.7263 0.6859 0.7289 0.6732 0.6999
MeanShift+MSRM 0.2843 0.3726 0.3225 0.8529 0.8021 0.8267 0.7046 0.7624 0.7323
本文算法 0.6754 0.9186 0.7784 0.8354 0.7980 0.8162 0.6830 0.7862 0.7309

Table 2

The algorithm complexity analysis"

算法 时间复杂度
NCUT O(N2)
SCOW O(N)
SLIC O(N)
SLIC+DBSCAN O(N+N2)
MeanShift+MSRM O(TN2)
本文算法 O(N+log2(‖E‖))
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