svm-image
所属分类 :模式识别(视觉/语音等)
开发工具 :C/C++
文件大小 :1020KB
下载次数 :655
上传日期 :2006-12-25 14:25:36
上 传 者 :
lcq2008
说明: 有关图像识别方法的实现,采用SVM方法完成图像的识别。 (the Image Recognition Methods of using SVM method to complete the image recognition.)
文件列表 :
svm-image (0, 2006-12-25) svm-image\LIBSVM.htm (8809, 2001-01-12) svm-image\WS_FTP.LOG (614, 2001-01-12) svm-image\libsvm-2.32 (0, 2006-09-23) svm-image\libsvm-2.32\COPYRIGHT (1498, 2001-01-12) svm-image\libsvm-2.32\heart_scale (27670, 2001-01-12) svm-image\libsvm-2.32\Makefile (419, 2001-01-12) svm-image\libsvm-2.32\svm-predict.c (2744, 2001-01-12) svm-image\libsvm-2.32\svm-scale.c (4721, 2001-01-12) svm-image\libsvm-2.32\svm-train.c (7941, 2001-01-12) svm-image\libsvm-2.32\svm.cpp (42525, 2001-01-12) svm-image\libsvm-2.32\svm.h (1335, 2001-01-12) svm-image\libsvm-2.32\WS_FTP.LOG (3321, 2001-01-12) svm-image\libsvm-2.32\windows (0, 2006-09-23) svm-image\libsvm-2.32\windows\svmpredict.exe (61440, 2001-01-12) svm-image\libsvm-2.32\windows\svmscale.exe (61440, 2001-01-12) svm-image\libsvm-2.32\windows\svmtoy.exe (86016, 2001-01-12) svm-image\libsvm-2.32\windows\svmtrain.exe (77824, 2001-01-12) svm-image\libsvm-2.32\windows\WS_FTP.LOG (1652, 2001-01-12) svm-image\libsvm-2.32\SVM_Train (0, 2006-09-23) svm-image\libsvm-2.32\SVM_Train\svm-train.c (7941, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\svm.cpp (42525, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\svm.h (1335, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\SVM_Train.dsp (4436, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\SVM_Train.dsw (543, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\SVM_Train.ncb (58368, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\SVM_Train.opt (53760, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\SVM_Train.plg (1631, 2001-01-12) svm-image\libsvm-2.32\SVM_Train\WS_FTP.LOG (3336, 2001-01-12) svm-image\libsvm-2.32\svm-toy (0, 2006-09-23) svm-image\libsvm-2.32\svm-toy\windows (0, 2006-09-23) svm-image\libsvm-2.32\svm-toy\windows\svm-toy.cpp (11033, 2001-01-12) svm-image\libsvm-2.32\svm-toy\windows\svm-toy.dsp (3479, 2001-01-12) svm-image\libsvm-2.32\svm-toy\windows\svm-toy.dsw (539, 2001-01-12) svm-image\libsvm-2.32\svm-toy\windows\svm-toy.ncb (66560, 2001-01-12) svm-image\libsvm-2.32\svm-toy\windows\svm-toy.opt (53760, 2001-01-12) svm-image\libsvm-2.32\svm-toy\windows\svm-toy.plg (935, 2001-01-12) svm-image\libsvm-2.32\svm-toy\windows\WS_FTP.LOG (2646, 2001-01-12) svm-image\libsvm-2.32\svm-toy\qt (0, 2006-09-23) ... ...
Libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It can solve C-SVM classification,
nu-SVM classification, one-class-SVM, epsilon-SVM regression,
and nu-SVM regression. This document explains the use of libsvm.
Libsvm is available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm
Please read the COPYRIGHT file before using libsvm.
Installation
============
On Unix systems, type `make' to build the `svm-train' and `svm-predict'
programs. Run them without arguments to show the usages of them.
On other systems, consult `Makefile' to build them or use the pre-built
binaries (Windows binaries are in the subdirectory `windows').
The format of training and testing data file is:
: : ...
.
.
.
is the target value of the training data. For classification,
it should be an integer which identifies a class (multi-class classification
is supported). For regression, it's any real number. For one-class SVM,
it's not used so can be any number. is an integer starting from 1,
is a real number. The labels in the testing data file are only used to
calculate accuracy or error. If they are unknown, just fill this column with a
number.
There is a sample training data for classification in this package:
heart_scale.
Type `svm-train heart_scale', and the program will read the training
data and output the model file `heart_scale.model'. Then you can
type `svm-predict heart_scale heart_scale.model output' to see the
rate of classification on training data. The `output' file contains
the prediction value of the model.
There are some other useful programs in this package.
svm-scale:
This is a tool for scaling input data file.
svm-toy:
This is a simple graphical interface which shows how SVM
separate data in a plane. You can click in the window to
draw data points. Use "change" button to choose class
1 or 2, "load" button to load data from a file, "save" button
to save data to a file, "run" button to obtain an SVM model,
and "clear" button to clear the window.
You can enter options in the bottom of the window, the syntax of
options is the same as `svm-train'.
Note that "load" and "save" consider data in the classification but
not the regression case. Each data point has one label (the color)
and two attributes (x-axis and y-axis values).
Type `make' in respective directories to build them.
You need Qt library to build the Qt version.
(You can download it from http://www.trolltech.com)
You need GTK+ library to build the GTK version.
(You can download it from http://www.gtk.org)
We use Visual C++ to build the Windows version.
The pre-built Windows binaries are in the windows subdirectory.
`svm-train' Usage
=================
Usage: svm-train [options] training_set_file [model_file]
options:
-s svm_type : set type of SVM (default 0)
0 -- C-SVC
1 -- nu-SVC
2 -- one-class SVM
3 -- epsilon-SVR
4 -- nu-SVR
-t kernel_type : set type of kernel function (default 2)
0 -- linear: u'*v
1 -- polynomial: (gamma*u'*v + coef0)^degree
2 -- radial basis function: exp(-gamma*|u-v|^2)
3 -- sigmoid: tanh(gamma*u'*v + coef0)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/k)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 40)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)
-wi weight: set the parameter C of class i to weight*C in C-SVC (default 1)
-v n: n-fold cross validation mode
The k in the -g option means the number of attributes in the input data.
option -v randomly splits the data into n parts and calculates cross
validation accuracy/mean squared error on them.
`svm-predict' Usage
===================
Usage: svm-predict test_file model_file output_file
model_file is the model file generated by svm-train.
test_file is the test data you want to predict.
svm-predict will produce output in the output_file.
No options are needed for svm-predict.
Tips on practical use
=====================
* Scale your data. For example, scale each attribute to [0,1] or [-1,+1].
* For C-SVC, try small and large C, like 1 to 1000 and decide which are
better for your data by cross validation. For the better C's, try
several gamma's.
* nu in nu-SVC/one-class-SVM/nu-SVR approximates the fraction of training
errors and support vectors.
* If data for classification are unbalanced (e.g. many positive and
few negative), try different penalty parameters C by -wi (see
examples below).
Examples
========
> svm-train -s 0 -c 1000 -t 1 -g 1 -r 1 -d 3 data_file
Train a classifier with polynomial kernel (u'v+1)^3 and C = 1000
> svm-train -s 1 -n 0.1 -t 2 -g 0.5 -e 0.00001 data_file
Train a classifier by nu-SVM (nu = 0.1) with RBF kernel
exp(-0.5|u-v|^2) and stopping tolerance 0.00001
> svm-train -s 3 -p 0.1 -t 0 -c 10 data_file
Solve SVM regression with linear kernel u'v and C=10, and epsilon = 0.1
in the loss function.
> svm-train -s 0 -c 10 -w1 1 -w-1 5 data_file
Train a classifier with penalty 10 for class 1 and penalty 50
for class -1.
> svm-train -s 0 -c 500 -g 0.1 -v 5 data_file
Do five-fold cross validation for the classifier using
the parameters C = 500 and gamma = 0.1
Library Usage
=============
These functions and structures are declared in the header file `svm.h'.
You need to #include "svm.h" in your C/C++ source files and link your
program with `svm.cpp'. You can see `svm-train.c' and `svm-predict.c'
for examples showing how to use them.
Before you classify test data, you need to construct an SVM model
(`svm_model') using training data. A model can also be saved in
a file for later use. Once an SVM model is available, you can use it
to classify new data.
- Function: struct svm_model *svm_train(const struct svm_problem *prob,
const struct svm_parameter *param);
This function constructs and returns an SVM model according to
the given training data and parameters.
struct svm_problem describes the problem:
struct svm_problem
{
int l;
double *y;
struct svm_node **x;
};
where `l' is the number of training data, and `y' is an array containing
their target values. (integers in classification, real numbers in
regression) `x' is an array of pointers, each of which points to a sparse
representation (array of svm_node) of one training vector.
For example, if we have the following training data:
LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5
----- ----- ----- ----- ----- -----
1 0 0.1 0.2 0 0
2 0 0.1 0.3 -1.2 0
1 0.4 0 0 0 0
2 0 0.1 0 1.4 0.5
3 -0.1 -0.2 0.1 1.1 0.1
then the components of svm_problem are:
l = 5
y -> 1 2 1 2 3
x -> [ ] -> (2,0.1) (3,0.2) (-1,?)
[ ] -> (2,0.1) (3,0.3) (4,-1.2) (-1,?)
[ ] -> (1,0.4) (-1,?)
[ ] -> (2,0.1) (4,1.4) (5,0.5) (-1,?)
[ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (-1,?)
where (index,value) is stored in the structure `svm_node':
struct svm_node
{
int index;
double value;
};
index = -1 indicates the end of one vector.
struct svm_parameter describes the parameters of an SVM model:
struct svm_parameter
{
int svm_type;
int kernel_type;
double degree; // for poly
double gamma; // for poly/rbf/sigmoid
double coef0; // for poly/sigmoid
// these are for training only
double cache_size; // in MB
double eps; // stopping criteria
double C; // for C_SVC, EPSILON_SVR, and NU_SVR
int nr_weight; // for C_SVC
int *weight_label; // for C_SVC
double* weight; // for C_SVC
double nu; // for NU_SVC, ONE_CLASS, and NU_SVR
double p; // for EPSILON_SVR
int shrinking; // use the shrinking heuristics
};
svm_type can be one of C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR.
C_SVC: C-SVM classification
NU_SVC: nu-SVM classification
ONE_CLASS: one-class-SVM
EPSILON_SVR: epsilon-SVM regression
NU_SVR: nu-SVM regression
kernel_type can be one of LINEAR, POLY, RBF, SIGMOID.
LINEAR: u'*v
POLY: (gamma*u'*v + coef0)^degree
RBF: exp(-gamma*|u-v|^2)
SIGMOID: tanh(gamma*u'*v + coef0)
cache_size is the size of the kernel cache, specified in megabytes.
C is the cost of constraints violation. (we usually use 1 to 1000)
eps is the stopping criterion. (we usually use 0.00001 in nu-SVC,
0.001 in others). nu is the parameter in nu-SVM, nu-SVR, and
one-class-SVM. p is the epsilon in epsilon-insensitive loss function
of epsilon-SVM regression. shrinking = 1 means shrinking is conducted;
= 0 otherwise.
nr_weight, weight_label, and weight are used to change the penalty
for some classes (If the weight for a class is not changed, it is
set to 1). This is useful for training classifier using unbalanced
input data or with asymmetric misclassification cost.
nr_weight is the number of elements in the array weight_label and
weight. Each weight[i] corresponds to weight_label[i], meaning that
the penalty of class weight_label[i] is scaled by a factor of weight[i].
If you do not want to change penalty for any of the classes,
just set nr_weight to 0.
*NOTE* Because svm_model contains pointers to svm_problem, you can
not free the memory used by svm_problem if you are still using the
svm_model produced by svm_train().
- Function: double svm_predict(const struct svm_model *model,
const struct svm_node *x);
This function does classification or regression on a test vector x
given a model.
For a classification model, the predicted class for x is returned.
For a regression model, the function value of x calculated using
the model is returned. For one-class model, +1 or -1 is returned.
- Function: int svm_save_model(const char *model_file_name,
const struct svm_model *model);
This function saves a model to a file; returns 0 on success, or -1
if an error occurs.
- Function: struct svm_model *svm_load_model(const char *model_file_name);
This function returns a pointer to the model read from the file,
or a null pointer if the model could not be loaded.
- Function: void svm_destroy_model(struct svm_model *model);
This function frees the memory used by a model.
Java version
============
The precompiled java class archive `libsvm.jar' and its source files are
in the java subdirectory. To run the programs, use
java -classpath libsvm.jar svm_train
java -classpath libsvm.jar svm_predict
java -classpath libsvm.jar svm_toy
We have tried IBM's and Sun's JDK.
You may need to add Java runtime library (like classes.zip) to the classpath.
You may need to increase maximum Java heap size.
Library usages are similar to the C version. These functions are available:
public class svm {
public static svm_model svm_train(svm_problem prob, svm_parameter param);
public static double svm_predict(svm_model model, svm_node[] x);
public static void svm_save_model(String model_file_name, svm_model model) throws IOException
public static svm_model svm_load_model(String model_file_name) throws IOException
}
The library is in the "libsvm" package.
Note that in Java version, svm_node[] is not ended with a node whose index = -1.
ADDITIONAL INFORMATION
============
Chih-Chung Chang and Chih-Jen Lin
LIBSVM : a library for support vector machines.
http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz
Acknowledgments:
This work was supported in part by the National Science
Council of Taiwan via the grant NSC 89-2213-E-002-013.
The authors thank Chih-Wei Hsu and Jen-Hao Lee
for many helpful discussions and comments.
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