Computer Science ›› 2020, Vol. 47 ›› Issue (2): 143-149.doi: 10.11896/jsjkx.190400121

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Fast Simple Linear Iterative Clustering for Image Superpixel Algorithm

LEI Tao1,LIAN Qian2,JIA Xiao-hong2,LIU Peng2   

  1. (School of Electronic Information and Artificial Intelligence Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China)1;
    (School of Electrical and Control Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China)2
  • Received:2019-04-22 Online:2020-02-15 Published:2020-03-18
  • About author:LEI Tao,born in 1981,Ph.D,professor,Ph.D supervisor,is member of CCF.His main research interests include image processing,pattern recognition and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61871259, 61811530325, 61461025, 61672333, 61873155).

Abstract: Simple linear iterative clustering (SLIC) takes a long time in the process of superpixel clustering.To address this drawback,this paper proposed a fast SLIC algorithm for image superpixel.Firstly,the algorithm removes the pixels that are clearly different from the clustering center in a superpixel area,and then uses the remaining pixels to update the clustering center.The operation ensures that the clustering center achieves convergence quickly,and prevents error propagation.Secondly,the edge pi-xels of each superpixel area are considered as active pixels while the non-edge pixels are considered as stable pixels that belong to one fixed class by initializing grids on the original image.Finally,fast superpixel image segmentation is achieved by labeling unstable pixels iteratively.This paper performed six comparative algorithms and the proposed algorithm on the Benchmark BSD500 under the environment of MATLAB.Compared with SLIC algorithm,the segmentation error rate of the proposed algorithm is reduced by 5%,the segmentation accuracy is improved by 0.5%,and the running time is 0.18s less than the later.The experimental results show that the proposed algorithm can improve the quality of superpixel segmentation while effectively reducing the computational complexity of the algorithm compared to popular superpixel algorithms.

Key words: Clustering, Image segmentation, SLIC, Superpixels

CLC Number: 

  • TP391
[1]LI P,YANG Y,FANG T.A fast superpixel algorithm with bia-sed-clustering using visual saliency[J].Journal of Xian Jiaotong University,2015,49(1):112-117.
[2]REN X,MALIK J.Learning a classification model for segmentation[C]∥Proceedings of International Conference on Computer Vision.2003:10-17.
[3]WANG S,LU H,YANG F,et al.Superpixel tracking[C]∥Proceedings of International Conference on Computer Vision.2011:1323-1330.
[4]HOIEM D,EFROS A,HEBERT M.Automatic photo pop-up.[J].ACM Transactions on Graphics,2015,24(3):577-584.
[5]SHI J,MALIK J.Normalized Cuts and Image Segmentation.[C]∥Conference on Computer Vision & Pattern Recognition.2000:888-905.
[6]MOORE A,PRINCE S,WARRELL J,et al.Superpixel lattices[C]∥Conference on Computer Vision & Pattern Recognition.2008:1-8.
[7]LIU M,TUZEL O,RAMALINGAM S,et al.Entropy rate superpixel segmentation[C]∥Conference on Computer Vision & Pattern Recognition.2011:2097-2104.
[8]VAN DEN BERGH M,BOIX X,ROIG G,et al.Seeds:Superpixels extracted via energy-driven sampling[C]∥Proceedings of the European Conference on Computer Vision.2012:13-26.
[9]VINCENT L,SOILLE P.Watersheds in digital spaces:an efficient algorithm based on immersion simulations[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(6):583-598.
[10]LEI T,ZHANG Y,WANG Y,et al.A conditionally invariant mathematical morphological framework for color images[J].Information Sciences,2017,387:34-52.
[11]LEI T,JIA X,LIU T,et al.Adaptive morphological reconstruction for seeded image segmentation[J].IEEE Transactions on Image Processing,2019,28(11):5510-5523.
[12]LEVINSHTEIN A,STERE A,KUTULAKOS K,et al.Tur-bopixels:Fast superpixels using geometric flows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(12):2290-2297.
[13]GONG Y,ZHOU Y.Differential evolutionary superpixel seg-mentation[J].IEEE Transactions on Image Processing,2018,27(3):1390-1404.
[14]WANG H.Superpixel segmentation with adaptive nonlocal random walk[J].IOP Conference Series Materials Science and Engineering,2018,435(1):012001.
[15]COMANICIU D,MEER P.Mean shift:A robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
[16]SHEN J,HAO X,LIANG Z,et al.Real-time superpixel segmentation by DBSCAN clustering algorithm[J].IEEE Transactions on Image Processing,2016,25(12):5933-5942.
[17]ACHANTA R,SHAJI A,SMITH K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[18]KANUNGO T,MOUNT D,NETANYAHU N,et al.An efficient k-means clustering algorithm:analysis and implementation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,24(7):881-892.
[19]BAN Z,LIU J,CAO L.Superpixel segmentation using gaussian mixture model[J].IEEE Transactions on Image Processing,2018,27(8):4105-4117.
[20]WU C,ZHANG L,ZHANG H,et al.Fuzzy SLIC:Fuzzy simple linear iterative clustering[J].arXiv:1812.10932.
[21]LI Z,CHEN J.Superpixel segmentation using linear spectral clustering[C]∥Conference on Computer Vision & Pattern Reco-gnition.2015:1356-1363.
[22]WEI X,YANG Q,GONG Y,et al.Superpixel hierarchy[J]. IEEE Transactions on Image Processing,2018,27(10):4838-4849.
[23]LIU Y,YU M,LI B,et al.Intrinsic manifold SLIC:a simple and efficient method for computing content-sensitive superpixels[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(3):653-666.
[24]JAMPANI V,SUN D,LIU M,et al.Superpixel sampling networks[C]∥Proceedings of the European Conference on Computer Vision.2018:352-368.
[25]LEI T,JIA X,ZHANG Y,et al.Superpixel-based fast fuzzy c-means clustering for color image segmentation[J].IEEE Tran-sactions on Fuzzy Systems,2019,27(9):1753-1766.
[26]ZHAO J,BO R,HOU Q,et al.FLIC:Fast linear iterative clustering with active search[J].Computational Visual Media,2018,4(4):333-348.
[27]LEE J.Digital image smoothing and the sigma filter[J].Computer Vision,Graphics,and Image Processing,1983,24:255-269.
[28]WANG J,WANG X.VCells:Simple and efficient superpixels using edge-weighted centroidal voronoi tessellations[J] IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(6):1241-1247.
[29]ACHANTA R,SUSSTRUNK S.Superpixels and polygons using simple non-iterative clustering[C]∥Conference on Computer Vision & Pattern Recognition.2017:4651-4660.
[30]MARTIN D,FOWLKES C,TAL D,et al.A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]∥Proceedings of International Conference on Computer Vision.2001:416-423.
[31]LIN T,MAIRE M,BELONGIE S,et al.Microsoft coco:common objects in context[C]∥Proceedings of the European Conference on Computer Vision.2014:740-755.
[1] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[2] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[3] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[4] MAO Sen-lin, XIA Zhen, GENG Xin-yu, CHEN Jian-hui, JIANG Hong-xia. FCM Algorithm Based on Density Sensitive Distance and Fuzzy Partition [J]. Computer Science, 2022, 49(6A): 285-290.
[5] CHEN Jing-nian. Acceleration of SVM for Multi-class Classification [J]. Computer Science, 2022, 49(6A): 297-300.
[6] CHEN Jia-zhou, ZHAO Yi-bo, XU Yang-hui, MA Ji, JIN Ling-feng, QIN Xu-jia. Small Object Detection in 3D Urban Scenes [J]. Computer Science, 2022, 49(6): 238-244.
[7] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[8] XING Yun-bing, LONG Guang-yu, HU Chun-yu, HU Li-sha. Human Activity Recognition Method Based on Class Increment SVM [J]. Computer Science, 2022, 49(5): 78-83.
[9] ZHU Zhe-qing, GENG Hai-jun, QIAN Yu-hua. Line-Segment Clustering Algorithm for Chemical Structure [J]. Computer Science, 2022, 49(5): 113-119.
[10] ZHANG Yu-jiao, HUANG Rui, ZHANG Fu-quan, SUI Dong, ZHANG Hu. Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization [J]. Computer Science, 2022, 49(5): 165-169.
[11] ZUO Yuan-lin, GONG Yue-jiao, CHEN Wei-neng. Budget-aware Influence Maximization in Social Networks [J]. Computer Science, 2022, 49(4): 100-109.
[12] YANG Xu-hua, WANG Lei, YE Lei, ZHANG Duan, ZHOU Yan-bo, LONG Hai-xia. Complex Network Community Detection Algorithm Based on Node Similarity and Network Embedding [J]. Computer Science, 2022, 49(3): 121-128.
[13] HAN Jie, CHEN Jun-fen, LI Yan, ZHAN Ze-cong. Self-supervised Deep Clustering Algorithm Based on Self-attention [J]. Computer Science, 2022, 49(3): 134-143.
[14] PU Shi, ZHAO Wei-dong. Community Detection Algorithm for Dynamic Academic Network [J]. Computer Science, 2022, 49(1): 89-94.
[15] ZHANG Ya-di, SUN Yue, LIU Feng, ZHU Er-zhou. Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index [J]. Computer Science, 2022, 49(1): 121-132.
Full text



No Suggested Reading articles found!