C# HMM使用Accord.Net进行手势识别

我在做手势识别项目。我的数据集由4个不同的手势组成，每个手势集包含大约70幅图像。我为每幅图像提取了4个特征。我正在尝试使用Accord.Net实现HMM，我知道我需要4个HMM，每个手势一个，但我不确定如何为学习/训练阶段构建特征向量序列。有人知道怎么解决吗

这是序列的简单代码：

double[][] sequences = new double[][] 
{
    new double[] { 0,1,2,3,4 }, // This is the first  sequence with label = 0 
    new double[] { 4,3,2,1,0 }, // This is the second sequence with label = 1
};

// Labels for the sequences 
int[] labels = { 0, 1 };

---

你是对的，每个手势都需要一个HMM。但是，如果您使用HiddenMarkovClassifier类（它是在您尝试检测的每个类之后创建的多个HMM的包装器），那么框架已经可以为您提供这种构造

如果每个图像有4个特征，则需要假设一个概率分布，该概率分布将能够对多变量特征进行建模。一个简单的选择是假设您的特征彼此独立，并且每个特征都遵循正态分布

因此，您可以使用以下示例代码来创建模型。它假设您的数据库只有两个训练序列，但实际上您必须有更多的训练序列

double[][][] sequences = new double[][][]
{
    new double[][] // This is the first  sequence with label = 0
    { 
        new double[] { 0, 1, 2, 1 }, // <-- this is the 4-features feature vector for
        new double[] { 1, 2, 5, 2 }, //       the first image of the first sequence
        new double[] { 2, 3, 2, 5 },
        new double[] { 3, 4, 1, 1 },
        new double[] { 4, 5, 2, 2 },
    }, 

    new double[][] // This is the second sequence with label = 1
    {
        new double[] { 4,  3, 4, 1 }, // <-- this is the 4-features feature vector for
        new double[] { 3,  2, 2, 2 }, //       the first image of the second sequence
        new double[] { 2,  1, 1, 1 },
        new double[] { 1,  0, 2, 2 },
        new double[] { 0, -1, 1, 2 },
    }
};

// Labels for the sequences
int[] labels = { 0, 1 };

// Create one base Normal distribution to be replicated accross the states
var initialDensity = new MultivariateNormalDistribution(4); // we have 4 features

// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<MultivariateNormalDistribution>(
    classes: 2, topology: new Forward(2), initial: initialDensity);

// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(
    classifier,

    // Train each model until the log-likelihood changes less than 0.0001
    modelIndex => new BaumWelchLearning<MultivariateNormalDistribution>(
        classifier.Models[modelIndex])
    {
        Tolerance = 0.0001,
        Iterations = 0,

        FittingOptions = new NormalOptions()
        {
            Diagonal = true,      // only diagonal covariance matrices
            Regularization = 1e-5 // avoid non-positive definite errors
        }

        // PS: Setting diagonal = true means the features will be
        // assumed independent of each other. This can also be
        // achieved by using an Independent<NormalDistribution>
        // instead of a diagonal multivariate Normal distribution
    }
);

// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);


// Calculate the probability that the given
//  sequences originated from the model
double likelihood, likelihood2;

// Try to classify the 1st sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out likelihood);

// Try to classify the 2nd sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out likelihood2);