========================================================================
Image Classification using Bag of Words and Spatial Pyramid BoW
Created by Piji Li (pagelee.sd@gmail.com)
Blog: 丕子 http://www.zhizhihu.com
QQ: 379115886
IRLab. : http://ir.sdu.edu.cn
Shandong University,Jinan,China
10/24/2011
Some code are from:
S. Lazebnik, C. Schmid, and J. Ponce, "Beyond Bags of Features: Spatial
Pyramid Matching for Recognizing Natural Scene Categories," CVPR 2006.
========================================================================
Just modify the ini.m: rootpath=your demo path, and then run main.m.
And remove all the files under the ../data/global/
The BOW and Dictionary is in the dir:/data/global, size of BOW_sift.mat is (DictionarySize * #images).
Size of dictionary.mat is (DictionarySize * dim of features).spatial_pyramid.mat is the Spatial Pyramid BoW.
In /data/local is the sift features for each images.
========================================================================
Classification using BOW rbf_svm
Accuracy = 75.8333% (91/120) (classification)
Classification using histogram intersection kernel svm
Accuracy = 82.5% (99/120) (classification)
Classification using Pyramid BOW rbf_svm
Accuracy = 82.5% (99/120) (classification)
Classification using Pyramid BOW histogram intersection kernel svm
Accuracy = 90% (108/120) (classification)
========================================================================
Idea from:
S. Lazebnik, C. Schmid, and J. Ponce, "Beyond Bags of Features: Spatial
Pyramid Matching for Recognizing Natural Scene Categories," CVPR 2006.
Images from:
Piji Li, Jun Ma, Shuai Gao. Actions in Still Web Images: Visualization, Detection and Retrieval.
The 12th International Conference on Web-Age InformationManagement (WAIM 2011). Springer, 2011.
SVM from:
Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines.
ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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温馨提示
使用matlab实现蜂群SVM,粒子群SVM,遗传算法SVM,数据包含有公共数据集,公共图像数据集,图像特征提取用了词袋,保证可用(公共图像数据集太大,删除了)
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蜂群SVM(ABC-SVM)遗传算法SVM(GA-SVM)粒子群SVM(PSO-SVM)图像数据集 词袋模型.rar (198个子文件)
runABC.asv 9KB
gentleboost_model.c 23KB
gentleboost_model.c 23KB
svmtrain.c 12KB
svmtrain.c 11KB
svmtrain.c 11KB
svmpredict.c 10KB
svmpredict.c 9KB
svmpredict.c 9KB
svm_model_matlab.c 8KB
gentleboost_predict.c 8KB
gentleboost_predict.c 8KB
svm_model_matlab.c 8KB
svm_model_matlab.c 8KB
libsvmread.c 4KB
libsvmread.c 4KB
hist_isect_c.c 3KB
libsvmwrite.c 2KB
libsvmwrite.c 2KB
COPYRIGHT 1KB
svm.cpp 63KB
svm.cpp 61KB
svm.cpp 61KB
.gitignore 12B
svm.h 3KB
svm.h 3KB
svm.h 3KB
svm_model_matlab.h 201B
svm_model_matlab.h 201B
svm_model_matlab.h 201B
rotateXLabels.m 13KB
ABC_SVM_exmp.m 11KB
CABC.m 10KB
ABCPSO.m 10KB
CCABCFun.m 10KB
CCABCFun.m 10KB
runABC.m 9KB
ABC_p_SVM_exmp.m 9KB
Copy_of_ABC_SVM_exmp.m 9KB
ABC_SVR_exmp.m 8KB
chapter_GA.m 6KB
lbp.m 6KB
SABC.m 5KB
SABC.m 5KB
psoSVM.m 4KB
GA_SVM.m 4KB
find_sift_grid.m 4KB
psoSVM.m 4KB
CalculateDictionary.m 4KB
fengqun1.m 4KB
fengqun1.m 4KB
CompilePyramid.m 3KB
gaSVMcgForClass.m 3KB
Copy_of_BOW.m 3KB
Run_svm.m 3KB
Tsallishuidu.m 3KB
gaSVMcgForClass.m 3KB
iris_svmsix.m 3KB
GenerateSiftDescriptors.m 3KB
getmapping.m 3KB
SVMcgForClass.m 3KB
Tsallis.m 2KB
main.m 2KB
do_assignment.m 2KB
do_p_classification__inter_svm.m 2KB
kfold.m 2KB
do_classification_inter_svm.m 2KB
SVMcgForClass.m 2KB
iris_svmsix.m 2KB
iris_svmsix.m 2KB
iris_svmsix.m 2KB
testFor_image_seg.m 2KB
Copy_of_ini.m 2KB
do_classification_rbf_svm.m 2KB
ini.m 2KB
do_classification_adaboost_pyramid_bow.m 1KB
do_classification_adaboost_bow.m 1KB
a_template_flow_usingSVM_class.m 1KB
do_p_classification__rbf_svm.m 1KB
EuclideanDistance.m 1KB
draw_cm.m 1KB
maxflash.m 1KB
maxflash.m 1KB
do_normalize.m 875B
pcaForSVM.m 847B
柱状图.m 770B
make.m 766B
Flash.m 754B
Flash.m 745B
hist_isect.m 729B
normalize_sift.m 632B
rosenbrock.m 602B
rastrigin.m 578B
MakeDataDirectory.m 569B
show_results_script.m 551B
g7x.m 512B
objfun.m 485B
Ackley.m 480B
scaleForSVM.m 462B
schwefel.m 447B
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资源评论
- TXYMQ2022-09-10非常有用的资源,有一定的参考价值,受益匪浅,值得下载。
- qq_402282872023-02-25超级好的资源,很值得参考学习,对我启发很大,支持!
- C_Y_Prefect2022-08-01感谢资源主的分享,这个资源对我来说很有用,内容描述详尽,值得借鉴。
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