三大计算机视觉和机器学习库的算法汇总

主要包括有OpenCV、Weka和Matlab，另外其中包含LibSVM、Vlfeat和DeepLearnToolbox等。

1.OpenCV

主页：http://opencv.org/
下载地址：http://opencv.org/downloads.html
编程环境：VS
版本：3.0.0
教程：doc\opencv_tutorials.pdf
API接口：doc\opencv2refman.pdf
特征检测：
• “FAST” – FastFeatureDetector
• “STAR” – StarFeatureDetector
• “SIFT” – SIFT (nonfree module)
• “SURF” – SURF (nonfree module)
• “ORB” – ORB
• “BRISK” – BRISK
• “MSER” – MSER
• “GFTT” – GoodFeaturesToTrackDetector
• “HARRIS” – GoodFeaturesToTrackDetector with Harris detector enabled
• “Dense” – DenseFeatureDetector
• “SimpleBlob” – SimpleBlobDetector
描述符提取：
• “SIFT” – SIFT
• “SURF” – SURF
• “BRIEF” – BriefDescriptorExtractor
• “BRISK” – BRISK
• “ORB” – ORB
• “FREAK” – FREAK
机器学习：
Normal Bayes Classifier
K-Nearest Neighbors
Support Vector Machines
Decision Trees
Boosting
Gradient Boosted Trees
Random Trees
Extremely randomized trees
Expectation Maximization
Neural Networks
kmeans

2.Weka

下载地址：http://www.cs.waikato.ac.nz/ml/weka/downloading.html
LibSVM下载地址：http://www.csie.ntu.edu.tw/~cjlin/libsvm/
编程环境：Eclipse
版本：3.6
教程：doc\index.html
机器学习：
1)weka.classifiers.bayes
AODE
AODEsr
BayesianLogisticRegression
BayesNet
ComplementNaiveBayes
DMNBtext
HNB
NaiveBayes
NaiveBayesMultinomial
NaiveBayesMultinomialUpdateable
NaiveBayesSimple
NaiveBayesUpdateable
WAODE
2)weka.classifiers.functions
GaussianProcesses
IsotonicRegression
LeastMedSq
LibLINEAR
LibSVM
LinearRegression
Logistic
MultilayerPerceptron
PaceRegression
PLSClassifier
RBFNetwork
SimpleLinearRegression
SimpleLogistic
SMO
SMOreg
SPegasos
VotedPerceptron
Winnow
3)weka.classifiers.lazy
IB1
IBk
KStar
LBR
LWL
4)weka.classifiers.meta
AdaBoostM1
AdditiveRegression
AttributeSelectedClassifier
Bagging
ClassificationViaClustering
ClassificationViaRegression
CostSensitiveClassifier
CVParameterSelection
Dagging
Decorate
END
FilteredClassifier
Grading
GridSearch
LogitBoost
MetaCost
MultiBoostAB
MultiClassClassifier
MultiScheme
OrdinalClassClassifier
RacedIncrementalLogitBoost
RandomCommittee
RandomSubSpace
RegressionByDiscretization
RotationForest
Stacking
StackingC
ThresholdSelector
Vote
5)weka.classifiers.mi
CitationKNN
MDD
MIBoost
MIDD
MIEMDD
MILR
MINND
MIOptimalBall
MISMO
MISVM
MIWrapper
SimpleMI
6)weka.classifiers.rules
ConjunctiveRule
DecisionTable
DecisionTableHashKey
DTNB
JRip
M5Rules
NNge
OneR
PART
Prism
Ridor
Rule
RuleStats
ZeroR
7)weka.classifiers.trees
ADTree
BFTree
DecisionStump
FT
Id3
J48
J48graft
LADTree
LMT
M5P
NBTree
RandomForest
RandomTree
REPTree
SimpleCart
UserClassifier
8)weka.clusterers
AbstractClusterer
AbstractDensityBasedClusterer
CheckClusterer
CLOPE
ClusterEvaluation
Cobweb
DBSCAN
EM
FarthestFirst
FilteredClusterer
HierarchicalClusterer
MakeDensityBasedClusterer
OPTICS
RandomizableClusterer
RandomizableDensityBasedClusterer
RandomizableSingleClustererEnhancer
sIB
SimpleKMeans
SingleClustererEnhancer
XMeans

3.Matlab

主页：http://www.mathworks.cn/index.html
工具箱说明文档：http://www.mathworks.cn/products/index.html?sec=category
用途说明文档：http://www.mathworks.cn/discovery/?s_tid=brdcrb

1)DeepLearnToolbox工具箱
下载地址：https://github.com/yangzhixuan/DeepLearnToolbox
教程：看包含的示例程序
CNN
SAE
DBN
CAE
NN

2)Vlfeat工具箱
下载地址：http://www.vlfeat.org/download.html
视觉特征：
HOG
SIFT
DSIFT
LIOP
MSER
机器学习：
GMM
K-means
AIB
Quick shift
SLIC
SVM
Forests of kd-trees

3)Neural Network Toolbox工具箱
所有关于神经网络的开发

4)其他基本工具箱
特征检测：
BRISK
FAST
Harris–Stephens
minimum eigenvalue
MSER
SURF
描述符提取：
extractFeatures
HOG
分类/回归：
Linear Regression
Nonlinear Regression
Generalized Linear Models
Classification Trees and Regression Trees
Discriminant Analysis
Naive Bayes Classification
Nearest Neighbors
Model Building and Assessment
聚类：
Hierarchical Clustering
k-Means Clustering
Gaussian Mixture Models
Hidden Markov Models
集成：
Boosting
Bagging
Random Subspace