使用Accord.Net進行手勢識別的HMM

我正在開發手勢識別項目。我的數據集由4種不同的手勢組成，其中每個手勢組包含大約70個圖像。我已經爲每個圖像提取了4個特徵。我試圖使用Accord.Net來實現HMM，並且我知道我需要4個HMM，每個手勢一個HMM，但我不確定如何構造用於學習/訓練的特徵向量序列相。有誰知道如何解決它？使用Accord.Net進行手勢識別的HMM

這是序列的簡單代碼：

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 };

來源

2014-02-05 user3144101

你是正確的，你需要一個HMM每個手勢。但是，如果使用HiddenMarkovClassifier類（它是在每個要嘗試檢測的類後創建的多個HMM的包裝器），則該框架已經可以爲您提供此構造。

如果每個圖像有4個特徵，則需要假設概率分佈能夠爲多元特徵建模。一個簡單的選擇就是假設你的特徵是相互獨立的，並且它們都遵循正態分佈。

因此，您可以使用下面的示例代碼來創建模型。它假定你的數據庫只有兩個訓練序列，但在現實中你必須有更多的許多。

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 };

上面的代碼顯示瞭如何設置學習數據庫。現在，一旦它已經被設置，你可以創建一個隱馬爾可夫分類器4個的正常功能（正態分佈之間的假設獨立）作爲

// 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 
    } 
);

最後，我們可以訓練模型，並在學習數據測試它的輸出：

// 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);

來源

2014-12-25 15:48:39 Cesar

使用Accord.Net進行手勢識別的HMM

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