Computer Science ›› 2018, Vol. 45 ›› Issue (7): 259-263.doi: 10.11896/j.issn.1002-137X.2018.07.045

• Graphics, Image & Pattem Recognition • Previous Articles     Next Articles

Image Segmentation Based on Saliency and Pulse Coupled Neural Network

WANG Yan ,XU Xian-fa   

  1. College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China
  • Received:2017-05-16 Online:2018-07-30 Published:2018-07-30

Abstract: Aiming at the problem that complicated images are interfered by background,an image segmentation method based on saliency and pulse coupled neural network (SPCNN) was proposed.Firstly,with the saliency filtering algorithm and the method of maximum between-class variance,the saliency map and the object image are obtained.Based on this,the interference which comes from the background for the initial seed point selection is eliminated.Secondly,according to saliency values in saliency map,the most saliency region is captured and the initial seed points are produced.Finally,the operations of object image segmentation are achieved via the improved RG-PCNN model.The experimental segmentation results of the gray natural images are obtained from the Berkeley segmentation dataset and ground truth database.There are three evaluating indicators:consistency coefficient(CC),similarity coefficient(SC) and integrate coefficient(IC).The experiment results show that the proposed model has better performance in terms of CC,SC and IC comparing with pulse coupled neural network (PCNN),region growing model (RG) and SPCNN.

Key words: Image segmentation, Pulse coupled neural network, Saliency, Seed points

CLC Number: 

  • TP183
[1]ZHAN K,SHI J H,LI Q Q,et al.Image Segmentation using Fast Linking SCM[C]∥International Joint Conference on Neural Networks.Ireland:IEEE Press,2015:1-8.
[2]CHEN Y L,PARK S K,MA Y D,et al.A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation[J].IEEE Transactions on Neural Networks,2011,22(6):880-892.
[3]ZHOU D,ZHOU H,GAO C,et al.Simplified parameters model of PCNN and its application to image segmentation[J].Pattern Analsysi and Applications,2016,19(4):939-951.
[4]ZHENG X,PENG Z M.Image segmentation based on activity degree with pulse coupled neural networks[J].Optics and Precision Engineering,2013,21(3):821-827.(in Chinese)
郑欣,彭真明.基于活跃度的脉冲耦合神经网络图像分割[J].光学精密工程,2013,21(3):821-827.
[5]AN Q,LI M,HE Y J,et al.Novel PCNN model and its application on imagesegmentation [J].Computer Science,2014,41(6A):215-217.(in Chinese)
安琦,李敏,何玉杰,等.一种优化脉冲耦合神经网络模型及在图像分割中的应用[J].计算机科学,2014,41(6A):215-217.
[6]WANG A W,SONG Y J.Image segmentation based on pulse coupled neural network[J].Computer Science,2017,44(4):317-322.(in Chinese)
王爱文,宋玉阶.基于脉冲耦合神经网络的图像分割[J].计算机科学,2017,44(4):317-322.
[7]XU G Z,ZHANG L,ZOU Y B,et al.Retinal blood segmentation with adaptive PCNN matched filter[J].Optics and Precision Engineering,2017,25(3):756-764.(in Chinese)
徐光柱,张柳,邹耀斌,等.自适应脉冲耦合神经网络与匹配滤波器相结合的视网膜血管分割[J].光学精密工程,2017,25(3):756-764.
[8]JIANG W,ZHOU H Y,SHEN Y,et al.Image segmentation with pulse-coupled neural network and Canny operators[J].Computers and Electrical Engineering,2015,46(C):528-538.
[9]HELMY A K,EL-TAWEEL G S.Image segmentation scheme based on SOM-PCNN in frequency domain[J].Applied Soft Computing,2016,40:405-415.
[10]STEWART R D,FERMIN I,OPPER M.Region Growing With Pulse-Coupled Neural Networks:An Alternative to Seeded Region Growing[J].IEEE Transactions on Neural Networks,2002,13(6):1557-1562.
[11]LU Y,MIAO J,DUAN L J,et al.A new approach to image segmentation based on simplified region growing PCNN[J].Applied Mathematics and Computation,2008,205(2):807-814.
[12]TONG N,LU H C,ZHANG L H.Saliency Detection withMulti-Scale Superpixels[J].IEEE Signal Processing Letters,2014,21(9):1035-1039.
[13]JOHNSON J L,PADGETT M L.PCNN Model and Applications[J].IEEE Transactions on Neural Networks,1999,10(3):480-497.
[14]WANG Z B,MA Y D,CHENG F Y.Review of pulse-coupled neural networks[J].Image and Vision Computing,2010,28(5):5-13.
[15]ADAMS R,BISCHOF L.Seeded region growing[J].IEEETransactions on Pattern Analysis & Machine Intelligence,1994,16(6):641-647.
[16]CHANG H H,ZHUANG A H,VALENTINO D J,et al.Performance measure characterization for evaluating neuroimage segmentation algorithms [J].Neuroimage,2009,47(1):122-135.
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