1

我一直在阅读有关 HMM 理论的内容。据我了解,我们需要初始概率、转移概率和发射概率来继续 HMM。我看到的有关 HMM 实现的示例在开始时定义了所有这些概率。但问题是我想使用 HMM 识别手势,但我无法弄清楚如何定义概率(即转换概率和发射概率矩阵)。我知道如何使用维特比算法来获得最佳序列或如何使用前后向后进行推理,这只是我担心的起始概率

任何人都可以指导我这件事。

4

2 回答 2

4

实际上隐藏马尔可夫模型有三种主要算法,您提到了三种算法中的两种:

  1. 前后向算法。
  2. 维特比算法
  3. Baum Welch 算法:从训练数据中推断参数(初始概率、转移概率、发射概率)。

Baum-Welch 算法基本上是一种期望最大化算法,您从随机起始参数开始,并使用前向后向算法计算初始参数的最大似然值,然后进行迭代。本讲义中介绍了该算法的伪代码的一个很好的说明。它还讨论了语音识别的相关问题,这是 HMM 的一个非常成功的应用。不幸的是,它没有讨论 Baum-Welch 或其他算法在实践中通常很难实现的事实,因为概率变得非常小。所以在实践中,你要么必须使用仔细的缩放,使用对数概率,要么使用 sci-kit learn 的 HMM 实现,其中包括所有三种主要的 HMM 算法。

于 2013-05-16T23:17:01.407 回答
1

Here you can check out my blog post which is a basic walk-through on how one can recognize gestures using HMM (It's from my experience gained while completing my undergraduate project). It highlights the basics of the three algorithms needed and also, how you can estimate the initial probabilities for the matrices involved eventually followed by the nature of training data and the learning algorithm. I hope it helps you get started at least.

Answering part of your question here, your estimate of the initial probabilities could vary from implementation to implementation but this largely depends on the nature of your Markov model, i.e, you would be working on one of the three types of models: Ergodic, LRB and LR. Depending on this, the initial values for your Transition, Emission and Initial Probabilites can vary. Next, in order to recognize the gesture you would need to "observe" a certain specific (or a group) of feature(s) in your captured image frames. I suggest you go through my blog post for a more detailed explanation for the same.

于 2013-05-29T04:17:35.980 回答