搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

结合稀疏编码和空间约束的红外图像聚类分割研究

宋长新 马克 秦川 肖鹏

引用本文:
Citation:

结合稀疏编码和空间约束的红外图像聚类分割研究

宋长新, 马克, 秦川, 肖鹏

Infrared image segmentation based on clustering combined with sparse coding and spatial constraints

Song Chang-Xin, Ma Ke, Qin Chuan, Xiao Peng
PDF
导出引用
  • 提出了结合稀疏编码和空间约束的红外图像聚类分割新算法, 在稀疏编码的基础上融合聚类算法, 扩展了传统的基于K-means聚类的图像分割方法. 结合稀疏编码的聚类分割算法能有效融合图像的局部信息, 便于利用像素之间的内在相关性, 但是对于分割会出现过分割和像素难以归类的问题.为此, 在字典的学习过程中, 将原子的聚类算法引入其中, 有助于缩减字典中原子所属类别的数目, 防止出现过分割; 考虑到像素及其邻域像素具有类别属性一致性的特点, 引入了空间类别属性约束信息, 并给出了一种交替优化算法. 联合学习字典、稀疏系数、聚类中心和隶属度, 将稀疏编码系数同原子对聚类中心的隶属程度相结合, 构造像素归属度来判断像素所属的类别. 实验结果表明, 该方法能够有效提高红外图像重要区域的分割效果, 具有较好的鲁棒性.
    A new algorithm for infrared image segmentation is proposed based on clustering combined with sparse coding and spatial constraints. The clustering algorithm is fused on the basis of sparse coding. The traditional image segmentation method based on K-means clustering is extended. The clustering algorithm combined with sparse coding can fuse the local information of image. The inner relationships between pixels are used. However, the problem of over-segmentation and difficulty in pixels classification for segmentation arise. The clustering method is introduced for atoms into dictionary learning. The class number of atoms in dictionary is reduced in order to avoid over-segmentation. The spatial class property information is also introduced by considering the property of the pixel, and the pixels in the neighbor region should have class coherent constraints. An alternate optimization algorithm is proposed to learn the dictionary, sparse coefficients, cluster center and degrees of membership jointly. Then the classes of pixels are estimated by constructing pixel ownership degrees, combining the sparse coefficients and the degrees of membership with the atoms to cluster center. The experimental results show that the important area can be separated well, and the proposed method has good robustness.
    • 基金项目: 青海省自然基金(批准号:2011-z-748)和青海省135高层次人才培养基金资助的课题.
    • Funds: Project supported by the Natural Science Foundation of Qinghai Province, China (Grant No. 2011-z-748) and the Fund for 135 High-Level Fostering Talents of Qinghai Province, China.
    [1]

    Wu Y Q, Zhang J K 2010 Acta Phys. Sin. 59 5495 (in Chinese) [吴一全, 张金矿 2010 物理学报 59 5495]

    [2]

    Liang Y M, Zhai H C, Chang S J, Zhang S Y 2003 Acta Phys. Sin. 52 11 (in Chinese) [梁艳梅, 翟宏琛, 常胜江, 张思远 2003 物理学报 52 11]

    [3]

    Song C X 2009 Microelectronics and Computer 26 60 (in Chinese) [宋长新 2009 微电子学与计算机 26 60]

    [4]

    Tang Y G, Di Q Y, Zhao L X 2009 Acta Phys. Sin. 58 15 (in Chinese) [唐英干, 邸秋艳, 赵立兴 2009 物理学报 58 15]

    [5]

    Ren J J, He M Y 2008 J. Infrared Millim. Waves 27 72 (in Chinese) [任继军, 何明一 2008红外与毫米波学报 27 72]

    [6]

    Fan J C, Han M, Wang J 2009 Pattern Recognition 42 2527

    [7]

    Wu J, Li J, Liu J, Tian J W 2004 International Conference on Robotics and Biomimetics Shenyang China August 22-26, 2004 p742

    [8]

    Jia J H, Jiao L C 2010 J. Infrared Millim. Waves 29 69 (in Chinese) [贾建华, 焦李成 2010 红外与毫米波学报 29 69]

    [9]

    Chen S C, Zhang D Q 2004 IEEE Trans. Syst. Man Cybern. 34 1907

    [10]

    Zhao F, Jiao L C, Liu H Q 2011 Frontiers of Computer Science in China 1 45

    [11]

    Yang J C, Yu K, Gong Y H, Huang T S 2009 IEEE Conference on Computer Vision and Pattern Recognition Miami, USA, June 20-25, 2009 p1794

    [12]

    Wright J, Ma Y, Mairal J, Spairo G, Huang T, Yan S 2010 Proc. IEEE 98 1031

    [13]

    Zhuang L S, Gao H Y, Liu C, Yu N H 2011 Journal of Software 22 (Suppl. 2) 89 (in Chinese) [庄连生, 高浩渊, 刘超, 俞能海 2011 软件学报 22 (增2) 89]

    [14]

    Zhao J J, Tang Z Y, Yang J, Liu E Q, Zhou Y 2011 J. Infrared Millim. Waves 30 156 (in Chinese) [赵佳佳, 唐峥远, 杨杰, 刘尔琦, 周越 2011 红外与毫米波学报 30 156]

    [15]

    Wang J J, Yang J C, Yu K, Lv F J, Huang T S, Gong Y H 2010 CVPR San Francisco, USA, June 13-18, 2010 p3360

    [16]

    Liu L Q, Wang L, Liu X W 2011 ICCV Barcelona, Spain, November 6-13, 2011 p2486

    [17]

    Gao S H, Tsang W H, Chia L T 2010 ECCV Crete, Greece, September 5-11, 2010 p566

    [18]

    Yuan L, Liu J, Ye J P 2011 NIPS Sierra, Nevada, December 16-17, 2011 p232

    [19]

    Lee H L, Battle A, Raina R, Andrew Y N 2007 NIPS Vancouver, B.C., Canada, December 3-6, 2007 p801

    [20]

    Ramirez I, Sprechmann P, Sapiro G 2010 CVPR San Francisco, CA June 13-18, 2010 p3501

    [21]

    Dahl A L, Larsen R 2011 BMVC Dundee, UK, August 29-September 2, 2011 p77.1

  • [1]

    Wu Y Q, Zhang J K 2010 Acta Phys. Sin. 59 5495 (in Chinese) [吴一全, 张金矿 2010 物理学报 59 5495]

    [2]

    Liang Y M, Zhai H C, Chang S J, Zhang S Y 2003 Acta Phys. Sin. 52 11 (in Chinese) [梁艳梅, 翟宏琛, 常胜江, 张思远 2003 物理学报 52 11]

    [3]

    Song C X 2009 Microelectronics and Computer 26 60 (in Chinese) [宋长新 2009 微电子学与计算机 26 60]

    [4]

    Tang Y G, Di Q Y, Zhao L X 2009 Acta Phys. Sin. 58 15 (in Chinese) [唐英干, 邸秋艳, 赵立兴 2009 物理学报 58 15]

    [5]

    Ren J J, He M Y 2008 J. Infrared Millim. Waves 27 72 (in Chinese) [任继军, 何明一 2008红外与毫米波学报 27 72]

    [6]

    Fan J C, Han M, Wang J 2009 Pattern Recognition 42 2527

    [7]

    Wu J, Li J, Liu J, Tian J W 2004 International Conference on Robotics and Biomimetics Shenyang China August 22-26, 2004 p742

    [8]

    Jia J H, Jiao L C 2010 J. Infrared Millim. Waves 29 69 (in Chinese) [贾建华, 焦李成 2010 红外与毫米波学报 29 69]

    [9]

    Chen S C, Zhang D Q 2004 IEEE Trans. Syst. Man Cybern. 34 1907

    [10]

    Zhao F, Jiao L C, Liu H Q 2011 Frontiers of Computer Science in China 1 45

    [11]

    Yang J C, Yu K, Gong Y H, Huang T S 2009 IEEE Conference on Computer Vision and Pattern Recognition Miami, USA, June 20-25, 2009 p1794

    [12]

    Wright J, Ma Y, Mairal J, Spairo G, Huang T, Yan S 2010 Proc. IEEE 98 1031

    [13]

    Zhuang L S, Gao H Y, Liu C, Yu N H 2011 Journal of Software 22 (Suppl. 2) 89 (in Chinese) [庄连生, 高浩渊, 刘超, 俞能海 2011 软件学报 22 (增2) 89]

    [14]

    Zhao J J, Tang Z Y, Yang J, Liu E Q, Zhou Y 2011 J. Infrared Millim. Waves 30 156 (in Chinese) [赵佳佳, 唐峥远, 杨杰, 刘尔琦, 周越 2011 红外与毫米波学报 30 156]

    [15]

    Wang J J, Yang J C, Yu K, Lv F J, Huang T S, Gong Y H 2010 CVPR San Francisco, USA, June 13-18, 2010 p3360

    [16]

    Liu L Q, Wang L, Liu X W 2011 ICCV Barcelona, Spain, November 6-13, 2011 p2486

    [17]

    Gao S H, Tsang W H, Chia L T 2010 ECCV Crete, Greece, September 5-11, 2010 p566

    [18]

    Yuan L, Liu J, Ye J P 2011 NIPS Sierra, Nevada, December 16-17, 2011 p232

    [19]

    Lee H L, Battle A, Raina R, Andrew Y N 2007 NIPS Vancouver, B.C., Canada, December 3-6, 2007 p801

    [20]

    Ramirez I, Sprechmann P, Sapiro G 2010 CVPR San Francisco, CA June 13-18, 2010 p3501

    [21]

    Dahl A L, Larsen R 2011 BMVC Dundee, UK, August 29-September 2, 2011 p77.1

  • [1] 刘鸿江, 刘逸飞, 谷付星. 基于深度学习的微纳光纤自动制备系统. 物理学报, 2024, 73(10): 104207. doi: 10.7498/aps.73.20240171
    [2] 张亚君, 蔡佳林, 乔亚, 曾中明, 袁喆, 夏钶. 基于磁性隧道结的群体编码实现无监督聚类. 物理学报, 2022, 71(14): 148506. doi: 10.7498/aps.71.20220252
    [3] 戴宇佳, 李明亮, 宋超, 高勋, 郝作强, 林景全. 空间约束结合梯度下降法提高铝合金中Fe成分激光诱导击穿光谱技术检测精度. 物理学报, 2021, 70(20): 205204. doi: 10.7498/aps.70.20210792
    [4] 夏正德, 宋娜, 刘宾, 潘晋孝, 闫文敏, 邵子惠. 基于字典学习的稠密光场重建算法. 物理学报, 2020, 69(6): 064201. doi: 10.7498/aps.69.20191621
    [5] 苏新宇, 王丽嘉, 朱艳春. 基于心脏电影磁共振图像的一种新的右心室多图谱分割方法. 物理学报, 2019, 68(19): 190701. doi: 10.7498/aps.68.20190582
    [6] 刘振, 孙超, 刘雄厚, 郭祺丽. 一种加权稀疏约束稳健Capon波束形成方法. 物理学报, 2016, 65(10): 104303. doi: 10.7498/aps.65.104303
    [7] 范虹, 朱艳春, 王芳梅, 张旭梅. 多分辨率水平集算法的乳腺MR图像分割. 物理学报, 2014, 63(11): 118701. doi: 10.7498/aps.63.118701
    [8] 邓承志, 田伟, 陈盼, 汪胜前, 朱华生, 胡赛凤. 基于局部约束群稀疏的红外图像超分辨率重建. 物理学报, 2014, 63(4): 044202. doi: 10.7498/aps.63.044202
    [9] 刘擎超, 陆建, 陈淑燕. 基于能力区域的交通状态预测方法. 物理学报, 2014, 63(14): 140504. doi: 10.7498/aps.63.140504
    [10] 张品, 梁艳梅, 常胜江, 范海伦. 基于能量最小化的肾脏计算断层扫描图像分割方法. 物理学报, 2013, 62(20): 208701. doi: 10.7498/aps.62.208701
    [11] 马原, 吕群波, 刘扬阳, 钱路路, 裴琳琳. 基于主成分变换的图像稀疏度估计方法. 物理学报, 2013, 62(20): 204202. doi: 10.7498/aps.62.204202
    [12] 吴涛, 金义富, 侯睿, 杨俊杰. 不确定性边缘表示与提取的认知物理学方法. 物理学报, 2013, 62(6): 064201. doi: 10.7498/aps.62.064201
    [13] 袁超, 柴毅. 基于簇相似度的网络社团结构探测算法. 物理学报, 2012, 61(21): 218901. doi: 10.7498/aps.61.218901
    [14] 吴一全, 张金矿. 二维直方图θ划分最大Shannon熵图像阈值分割. 物理学报, 2010, 59(8): 5487-5495. doi: 10.7498/aps.59.5487
    [15] 杨晓苹, 高丽娟, 王晓雷, 翟宏琛, 王明伟. 基于双相位编码的单通道彩色图像加密. 物理学报, 2009, 58(3): 1662-1667. doi: 10.7498/aps.58.1662
    [16] 唐英干, 邸秋艳, 赵立兴, 关新平, 刘福才. 基于二维最小Tsallis交叉熵的图像阈值分割方法. 物理学报, 2009, 58(1): 9-15. doi: 10.7498/aps.58.9
    [17] 刘赵杰, 邵 健, 张鹏远, 赵庆卫, 颜永红, 冯 稷. 汉语自然口语中声调识别的研究. 物理学报, 2007, 56(12): 7064-7069. doi: 10.7498/aps.56.7064
    [18] 蔡俊伟, 胡寿松, 陶洪峰. 基于选择性支持向量机集成的混沌时间序列预测. 物理学报, 2007, 56(12): 6820-6827. doi: 10.7498/aps.56.6820
    [19] 张军峰, 胡寿松. 基于一种新型聚类算法的RBF神经网络混沌时间序列预测. 物理学报, 2007, 56(2): 713-719. doi: 10.7498/aps.56.713
    [20] 梁艳梅, 翟宏琛, 常胜江, 张思远. 基于最大隶属度原则的彩色图像分割方法. 物理学报, 2003, 52(11): 2655-2659. doi: 10.7498/aps.52.2655
计量
  • 文章访问数:  5733
  • PDF下载量:  1145
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-05-03
  • 修回日期:  2012-10-04
  • 刊出日期:  2013-02-05

/

返回文章
返回