Classification Algorithms

The OpenCV Machine Learning module provides several powerful classification algorithms for supervised learning tasks.

SVM - Support Vector Machines

Support Vector Machines are powerful classifiers that work well for both linear and non-linear classification problems.

Creating an SVM Model

Ptr<SVM> svm = SVM::create();

Key Methods

setType

void

Sets the type of SVM formulationParameters:

val (int): SVM type. See SVM::Types

Types:

SVM::C_SVC (100): C-Support Vector Classification for n-class classification
SVM::NU_SVC (101): ν-Support Vector Classification with parameter ν
SVM::ONE_CLASS (102): Distribution Estimation (One-class SVM)
SVM::EPS_SVR (103): ε-Support Vector Regression
SVM::NU_SVR (104): ν-Support Vector Regression

setKernel

void

Initialize with one of predefined kernelsParameters:

kernelType (int): Kernel type. See SVM::KernelTypes

Kernel Types:

SVM::LINEAR (0): Linear kernel, no mapping done
SVM::POLY (1): Polynomial kernel
SVM::RBF (2): Radial basis function, good choice in most cases
SVM::SIGMOID (3): Sigmoid kernel
SVM::CHI2 (4): Exponential Chi2 kernel
SVM::INTER (5): Histogram intersection kernel

setC

void

Sets parameter C of a SVM optimization problemParameters:

val (double): Parameter C for C_SVC, EPS_SVR or NU_SVR

setGamma

void

Sets parameter γ of a kernel functionParameters:

val (double): Parameter gamma for POLY, RBF, SIGMOID or CHI2 kernels

train

bool

Trains the SVM modelParameters:

trainData (Ptr<TrainData>): Training data
flags (int): Optional flags

Returns: bool - true if training succeeded

predict

float

Predicts response for input samplesParameters:

samples (InputArray): Input samples, floating-point matrix
results (OutputArray): Optional output matrix of results
flags (int): Optional flags

Returns: float - predicted response for single sample

trainAuto

bool

Trains an SVM with optimal parameters using cross-validationParameters:

data (Ptr<TrainData>): Training data
kFold (int): Cross-validation parameter (default: 10)
Cgrid (ParamGrid): Grid for C parameter
gammaGrid (ParamGrid): Grid for gamma parameter
pGrid (ParamGrid): Grid for p parameter
nuGrid (ParamGrid): Grid for nu parameter
coeffGrid (ParamGrid): Grid for coeff parameter
degreeGrid (ParamGrid): Grid for degree parameter
balanced (bool): Create balanced cross-validation subsets

Returns: bool - true if training succeeded

getSupportVectors

Mat

Retrieves all the support vectorsReturns: Mat - matrix where support vectors are stored as rows

Example Usage

C++
Python

#include <opencv2/ml.hpp>

using namespace cv::ml;

// Create and configure SVM
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::RBF);
svm->setGamma(0.5);
svm->setC(1.0);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));

// Train the SVM
Ptr<TrainData> trainData = TrainData::create(samples, ROW_SAMPLE, labels);
svm->train(trainData);

// Predict
Mat results;
svm->predict(testSamples, results);

import cv2 as cv
import numpy as np

# Create and configure SVM
svm = cv.ml.SVM_create()
svm.setType(cv.ml.SVM_C_SVC)
svm.setKernel(cv.ml.SVM_RBF)
svm.setGamma(0.5)
svm.setC(1.0)
svm.setTermCriteria((cv.TERM_CRITERIA_MAX_ITER, 100, 1e-6))

# Train the SVM
svm.train(samples, cv.ml.ROW_SAMPLE, labels)

# Predict
result = svm.predict(test_samples)

KNearest - K-Nearest Neighbors

The K-Nearest Neighbors algorithm finds the k nearest neighbors and predicts the response based on their values.

Creating a KNearest Model

Ptr<KNearest> knn = KNearest::create();

Key Methods

setDefaultK

void

Sets the default number of neighbors to use in predict methodParameters:

val (int): Number of neighbors (must be greater than 1)

setIsClassifier

void

Sets whether classification or regression model should be trainedParameters:

val (bool): true for classification, false for regression

setAlgorithmType

void

Sets the algorithm typeParameters:

val (int): Algorithm type

Types:

KNearest::BRUTE_FORCE (1): Brute force search
KNearest::KDTREE (2): KD-tree based search

findNearest

float

Finds the neighbors and predicts responses for input vectorsParameters:

samples (InputArray): Input samples (rows are samples)
k (int): Number of nearest neighbors
results (OutputArray): Vector with prediction results
neighborResponses (OutputArray): Optional output for neighbor responses
dist (OutputArray): Optional output distances to neighbors

Returns: float - predicted value for single input vector

Example Usage

#include <opencv2/ml.hpp>

using namespace cv::ml;

// Create KNN classifier
Ptr<KNearest> knn = KNearest::create();
knn->setDefaultK(5);
knn->setIsClassifier(true);
knn->setAlgorithmType(KNearest::BRUTE_FORCE);

// Train
knn->train(trainSamples, ROW_SAMPLE, trainLabels);

// Predict
Mat results, neighbors, distances;
knn->findNearest(testSamples, 5, results, neighbors, distances);

DTrees - Decision Trees

Decision trees are tree-based classifiers that split data based on feature values.

Creating a Decision Tree Model

Ptr<DTrees> dtree = DTrees::create();

Key Methods

setMaxDepth

void

Sets the maximum possible depth of the treeParameters:

val (int): Maximum depth (root node has depth 0)

setMinSampleCount

void

Sets the minimum number of samples required to split a nodeParameters:

val (int): Minimum sample count (default: 10)

setMaxCategories

void

Sets the maximum number of categories for clusteringParameters:

val (int): Maximum categories (default: 10)

setCVFolds

void

Sets the number of folds for cross-validation pruningParameters:

val (int): Number of folds (default: 10)

setPriors

void

Sets a priori class probabilitiesParameters:

val (Mat): Array of class probabilities sorted by label

Example Usage

// Create and configure decision tree
Ptr<DTrees> dtree = DTrees::create();
dtree->setMaxDepth(10);
dtree->setMinSampleCount(10);
dtree->setCVFolds(10);

// Train
dtree->train(trainData);

// Predict
float response = dtree->predict(testSample);

Boost - Boosted Trees

Boosted tree classifier that combines multiple weak classifiers into a strong one.

Creating a Boost Model

Ptr<Boost> boost = Boost::create();

Key Methods

setBoostType

void

Sets the type of boosting algorithmParameters:

val (int): Boost type

Types:

Boost::DISCRETE (0): Discrete AdaBoost
Boost::REAL (1): Real AdaBoost (default, works well with categorical data)
Boost::LOGIT (2): LogitBoost (good for regression)
Boost::GENTLE (3): Gentle AdaBoost (good with regression data)

setWeakCount

void

Sets the number of weak classifiersParameters:

val (int): Number of weak classifiers (default: 100)

setWeightTrimRate

void

Sets the threshold for computational time savingsParameters:

val (double): Weight trim rate between 0 and 1 (default: 0.95)

Example Usage

// Create and configure Boost
Ptr<Boost> boost = Boost::create();
boost->setBoostType(Boost::REAL);
boost->setWeakCount(100);
boost->setWeightTrimRate(0.95);
boost->setMaxDepth(1);

// Train
boost->train(trainData);

// Predict
float result = boost->predict(testSample);

RTrees - Random Trees (Random Forest)

Random Forest is an ensemble learning method that constructs multiple decision trees.

Creating a Random Trees Model

Ptr<RTrees> rtrees = RTrees::create();

Key Methods

setCalculateVarImportance

void

Enable/disable variable importance calculationParameters:

val (bool): true to calculate variable importance

setActiveVarCount

void

Sets the size of randomly selected subset of features at each tree nodeParameters:

val (int): Number of active variables (0 = sqrt of total features)

setTermCriteria

void

Sets termination criteria for trainingParameters:

val (TermCriteria): Criteria specifying max iterations or accuracy

getVarImportance

Mat

Returns the variable importance arrayReturns: Mat - variable importance vector (if enabled during training)

getVotes

void

Returns the result of each individual tree in the forestParameters:

samples (InputArray): Samples for which votes will be calculated
results (OutputArray): Matrix where results will be written
flags (int): Flags for defining the type of RTrees

Example Usage

// Create and configure Random Forest
Ptr<RTrees> rtrees = RTrees::create();
rtrees->setMaxDepth(10);
rtrees->setMinSampleCount(10);
rtrees->setCalculateVarImportance(true);
rtrees->setActiveVarCount(0); // sqrt of total features
rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 0));

// Train
rtrees->train(trainData);

// Get variable importance
Mat varImportance = rtrees->getVarImportance();

// Predict
float result = rtrees->predict(testSample);

NormalBayesClassifier - Naive Bayes

Bayes classifier for normally distributed data using Bayesian statistics.

Creating a Naive Bayes Model

Ptr<NormalBayesClassifier> bayes = NormalBayesClassifier::create();

Key Methods

train

bool

Trains the Bayes classifierParameters:

trainData (Ptr<TrainData>): Training data
flags (int): Optional flags

Returns: bool - true if training succeeded

predict

float

Predicts response for input samplesParameters:

samples (InputArray): Input samples
results (OutputArray): Output predictions
flags (int): Optional flags

Returns: float - predicted class for single sample

predictProb

float

Predicts the response and returns probabilitiesParameters:

inputs (InputArray): Input vectors (one or more)
outputs (OutputArray): Predicted classes
outputProbs (OutputArray): Output probabilities for each class
flags (int): Optional flags

Returns: float - predicted class for single input

Example Usage

// Create Naive Bayes classifier
Ptr<NormalBayesClassifier> bayes = NormalBayesClassifier::create();

// Train
bayes->train(trainData);

// Predict with probabilities
Mat results, probs;
bayes->predictProb(testSamples, results, probs);

The Naive Bayes classifier assumes that features are normally distributed and independent. It works best when these assumptions hold true.

Core

Image Processing

Video Analysis

Camera Calibration

Object Detection

Deep Learning

Machine Learning

Classification Algorithms

SVM - Support Vector Machines

Creating an SVM Model

Key Methods

Example Usage

KNearest - K-Nearest Neighbors

Creating a KNearest Model

Key Methods

Example Usage

DTrees - Decision Trees

Creating a Decision Tree Model

Key Methods

Example Usage

Boost - Boosted Trees

Creating a Boost Model

Key Methods

Example Usage

RTrees - Random Trees (Random Forest)

Creating a Random Trees Model

Key Methods

Example Usage

NormalBayesClassifier - Naive Bayes

Creating a Naive Bayes Model

Key Methods

Example Usage

See Also

Build docs developers (and LLMs) love

Core

Image Processing

Video Analysis

Camera Calibration

Object Detection

Deep Learning

Machine Learning

​SVM - Support Vector Machines

​Creating an SVM Model

​Key Methods

​Example Usage

​KNearest - K-Nearest Neighbors

​Creating a KNearest Model

​Key Methods

​Example Usage

​DTrees - Decision Trees

​Creating a Decision Tree Model

​Key Methods

​Example Usage

​Boost - Boosted Trees

​Creating a Boost Model

​Key Methods

​Example Usage

​RTrees - Random Trees (Random Forest)

​Creating a Random Trees Model

​Key Methods

​Example Usage

​NormalBayesClassifier - Naive Bayes

​Creating a Naive Bayes Model

​Key Methods

​Example Usage

​See Also

Build docs developers (and LLMs) love

SVM - Support Vector Machines

Creating an SVM Model

Key Methods

Example Usage

KNearest - K-Nearest Neighbors

Creating a KNearest Model

Key Methods

Example Usage

DTrees - Decision Trees

Creating a Decision Tree Model

Key Methods

Example Usage

Boost - Boosted Trees

Creating a Boost Model

Key Methods

Example Usage

RTrees - Random Trees (Random Forest)

Creating a Random Trees Model

Key Methods

Example Usage

NormalBayesClassifier - Naive Bayes

Creating a Naive Bayes Model

Key Methods

Example Usage

See Also