BagOfVisualWords Class

[SerializableAttribute]
public class BagOfVisualWords : BaseBagOfVisualWords<BagOfVisualWords, SpeededUpRobustFeaturePoint, double[], IUnsupervisedLearning<IClassifier<double[], int>, double[], int>, SpeededUpRobustFeaturesDetector>

<SerializableAttribute>
Public Class BagOfVisualWords
	Inherits BaseBagOfVisualWords(Of BagOfVisualWords, SpeededUpRobustFeaturePoint, Double(), IUnsupervisedLearning(Of IClassifier(Of Double(), Integer), Double(), Integer), SpeededUpRobustFeaturesDetector)

// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;

// The Bag-of-Visual-Words model converts images of arbitrary 
// size into fixed-length feature vectors. In this example, we
// will be setting the codebook size to 10. This means all feature
// vectors that will be generated will have the same length of 10.

// By default, the BoW object will use the sparse SURF as the 
// feature extractor and K-means as the clustering algorithm.

// Create a new Bag-of-Visual-Words (BoW) model
BagOfVisualWords bow = new BagOfVisualWords(10);
// Note: the BoW model can also be created using
// var bow = BagOfVisualWords.Create(10);

// Get some training images
Bitmap[] images = GetImages();

// Compute the model
bow.Learn(images);

// After this point, we will be able to translate
// images into double[] feature vectors using
double[][] features = bow.Transform(images);

// Now, the features can be used to train any classification
// algorithm as if they were the images themselves. For example,
// let's assume the first three images belong to a class and
// the second three to another class. We can train an SVM using

int[] labels = { -1, -1, -1, +1, +1, +1 };

// Create the SMO algorithm to learn a Linear kernel SVM
var teacher = new SequentialMinimalOptimization<Linear>()
{
    Complexity = 10000 // make a hard margin SVM
};

// Obtain a learned machine
var svm = teacher.Learn(features, labels);

// Use the machine to classify the features
bool[] output = svm.Decide(features);

// Compute the error between the expected and predicted labels
double error = new ZeroOneLoss(labels).Loss(output);

// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;

// The Bag-of-Visual-Words model converts images of arbitrary 
// size into fixed-length feature vectors. In this example, we
// will be setting the codebook size to 10. This means all feature
// vectors that will be generated will have the same length of 10.

// By default, the BoW object will use the sparse SURF as the 
// feature extractor and K-means as the clustering algorithm.
// In this example, we will use the Binary-Split clustering
// algorithm instead.

// Create a new Bag-of-Visual-Words (BoW) model
var bow = BagOfVisualWords.Create(new BinarySplit(10));

// Since we are using generics, we can setup properties 
// of the binary split clustering algorithm directly:
bow.Clustering.ComputeProportions = true;
bow.Clustering.ComputeCovariances = false;

// Get some training images
Bitmap[] images = GetImages();

// Compute the model
bow.Learn(images);

// After this point, we will be able to translate
// images into double[] feature vectors using
double[][] features = bow.Transform(images);

// Now, the features can be used to train any classification
// algorithm as if they were the images themselves. For example,
// let's assume the first three images belong to a class and
// the second three to another class. We can train an SVM using

int[] labels = { -1, -1, -1, +1, +1, +1 };

// Create the SMO algorithm to learn a Linear kernel SVM
var teacher = new SequentialMinimalOptimization<Linear>()
{
    Complexity = 10000 // make a hard margin SVM
};

// Obtain a learned machine
var svm = teacher.Learn(features, labels);

// Use the machine to classify the features
bool[] output = svm.Decide(features);

// Compute the error between the expected and predicted labels
double error = new ZeroOneLoss(labels).Loss(output); // should be 0

Accord.Math.Random.Generator.Seed = 0;

// The Bag-of-Visual-Words model converts images of arbitrary 
// size into fixed-length feature vectors. In this example, we
// will be setting the codebook size to 10. This means all feature
// vectors that will be generated will have the same length of 10.

// By default, the BoW object will use the sparse SURF as the 
// feature extractor and K-means as the clustering algorithm.
// In this example, we will use the HOG feature extractor
// and the Binary-Split clustering algorithm instead.

// Create a new Bag-of-Visual-Words (BoW) model using HOG features
var bow = BagOfVisualWords.Create(new HistogramsOfOrientedGradients(), new BinarySplit(10));

// Get some training images
Bitmap[] images = GetImages();

// Compute the model
bow.Learn(images);

// After this point, we will be able to translate
// images into double[] feature vectors using
double[][] features = bow.Transform(images);

// Now, the features can be used to train any classification
// algorithm as if they were the images themselves. For example,
// let's assume the first three images belong to a class and
// the second three to another class. We can train an SVM using

int[] labels = { -1, -1, -1, +1, +1, +1 };

// Create the SMO algorithm to learn a Linear kernel SVM
var teacher = new SequentialMinimalOptimization<Linear>()
{
    Complexity = 100 // make a hard margin SVM
};

// Obtain a learned machine
var svm = teacher.Learn(features, labels);

// Use the machine to classify the features
bool[] output = svm.Decide(features);

// Compute the error between the expected and predicted labels
double error = new ZeroOneLoss(labels).Loss(output); // should be 0

Accord.Math.Random.Generator.Seed = 0;

// The Bag-of-Visual-Words model converts images of arbitrary 
// size into fixed-length feature vectors. In this example, we
// will be setting the codebook size to 10. This means all feature
// vectors that will be generated will have the same length of 10.

// By default, the BoW object will use the sparse SURF as the 
// feature extractor and K-means as the clustering algorithm.
// In this example, we will use the FREAK feature extractor
// and the K-Modes clustering algorithm instead.

// Create a new Bag-of-Visual-Words (BoW) model using FREAK binary features
var bow = BagOfVisualWords.Create<FastRetinaKeypointDetector, KModes<byte>, byte[]>(
    new FastRetinaKeypointDetector(), new KModes<byte>(10, new Hamming()));

// Get some training images
Bitmap[] images = GetImages();

// Compute the model
bow.Learn(images);

// After this point, we will be able to translate
// images into double[] feature vectors using
double[][] features = bow.Transform(images);

// Now, the features can be used to train any classification
// algorithm as if they were the images themselves. For example,
// let's assume the first three images belong to a class and
// the second three to another class. We can train an SVM using

int[] labels = { -1, -1, -1, +1, +1, +1 };

// Create the SMO algorithm to learn a Linear kernel SVM
var teacher = new SequentialMinimalOptimization<Linear>()
{
    Complexity = 1000 // make a hard margin SVM
};

// Obtain a learned machine
var svm = teacher.Learn(features, labels);

// Use the machine to classify the features
bool[] output = svm.Decide(features);

// Compute the error between the expected and predicted labels
double error = new ZeroOneLoss(labels).Loss(output); // should be 0