基于libsvm的图像分割.doc

测试数据来源：MATLAB高效编程技巧与应用：25个案例分析吴鹏(著) | 北京航空航天大学出版社MATLAB统计分析与应用：40个案例分析谢中华(著) | 北京航空航天大学出版社该帖子中测试程序需要使用如下工具箱libsvm-mat-2.89-3FarutoUltimate3.0/thread-9327-1-1.html更多关于SVM的事情请看关于SVM的那点破事长期更新整理 by faruto/thread-10966-1-1.html测试图片：现用libsvm来实现图像分割，测试图片用的亦是25cases和40cases中的那个littleduck测试图片。主体程序思想为25cases中的代码过程，改进之处为可以让用户利用ginput来提取背景的样本点和前景（待分割出来的目标）的样本点作为训练样本，而不需实现指定背景和前景的样本点，也不用额外的小软件来查看某点的RGB值，ginput即可。O(_)O 测试代码：本帖隐藏的内容1. % ImSegmentLibsvm2. % a litter test of image segment based on libsvm3. % by faruto4. % Email:5. % QQ:5166674086. % /faruto7. % last modified 2010.11.058. % 9. % www.mfun.la10. %11. tic;12. close all;13. clear;14. clc;15. format compact;16. %17. pic = imread(littleduck.jpg);18. figure;19. imshow(pic);20. % 确定训练集21.22. TrainData_background = zeros(20,3,double);23. TrainData_foreground = zeros(20,3,double);24.25. % 背景采样26. msgbox(Please get 20 background samples,Background Samples,help);27. pause;28. for run = 1:2029. x,y = ginput(1);30. hold on;31. plot(x,y,r*);32. x = uint8(x);33. y = uint8(y);34. TrainData_background(run,1) = pic(x,y,1);35. TrainData_background(run,2) = pic(x,y,2);36. TrainData_background(run,3) = pic(x,y,3);37. end38. % 待分割出来的前景采样39. msgbox(Please get 20 foreground samples which is the part to be segmented,Foreground Samples,help);40. pause;41. for run = 1:2042. x,y = ginput(1);43. hold on;44. plot(x,y,ro);45. x = uint8(x);46. y = uint8(y);47. TrainData_foreground(run,1) = pic(x,y,1);48. TrainData_foreground(run,2) = pic(x,y,2);49. TrainData_foreground(run,3) = pic(x,y,3);50. end51. % % 背景训练样本 10*352. % TrainData_background = .53. % 52 74 87;54. % 76 117 150;55. % 19 48 62;56. % 35 64 82;57. % 46 58 36;58. % 50 57 23;59. % 110 127 135;60. % 156 173 189;61. % 246 242 232;62. % 166 174 151;63. % % 前景训练样本 8*364. % TrainData_foreground = .65. % 211 192 107;66. % 202 193 164;67. % 32 25 0;68. % 213 201 151;69. % 115 75 16;70. % 101 70 0;71. % 169 131 22;72. % 150 133 87;73.74. % let background be 0 & foreground 175. TrainLabel = zeros(length(TrainData_background),1); .76. ones(length(TrainData_foreground),1);77. % 建立支持向量机 基于libsvm78. TrainData = TrainData_background;TrainData_foreground;79.80. model = svmtrain(TrainLabel, TrainData, -t 1 -d 1);81. % 进行预测 i.e.进行图像分割 基于libsvm82. preTrainLabel = svmpredict(TrainLabel, TrainData, model);83. m,n,k = size(pic);84. TestData = double(reshape(pic,m*n,k);85. TestLabal = svmpredict(zeros(length(TestData),1), TestData, model);86. %87. ind = reshape(TestLabal,TestLabal,TestLabal,m,n,k);88. ind = logical(ind);89. pic_seg = pic;90. pic_seg(ind) =