A comparison of HMM, Naïve Bayesian, and Markov model in exploiting knowledge content in digital ink: A case study on handwritten music notation recognition
A comparison of HMM, Naïve Bayesian, and Markov model in exploiting knowledge content in digital ink: A case study on handwritten music notation recognition
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HMM、朴素贝叶斯和马尔可夫模型在利用数字墨水中的知识内容方面的比较：手写乐谱识别的案例研究
DOI：
10.1109/icme.2010.5583292
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Choo
中科院分区：
文献类型：
--
作者：
K. Lee;S. Phon;Choo
模型的性能不仅取决于模型可用知识的数量，还取决于如何利用这些知识。我们研究了基于三种相关概率推理技术的手写乐谱识别：隐马尔可夫模型（hmm），马尔可夫模型（mm）和Naïve贝叶斯（NBs）。乐谱写在写字板上。代表这个符号的一系列墨水图案被捕获，随后被用于构建hmm、mm和nb的模型。所提出的方法利用了从墨水模式中获得的全局和局部信息，我们已经通过不同hmm中使用的不同特征展示了对这些信息的利用。使用未见过的测试数据集比较这些分类模型的特异性和敏感性。研究结果表明，HMM优于MM和NB模型，因为HMM能够利用转移概率（转移矩阵A）和观测事件的整体可能性（发射矩阵B）。此外，具有更多隐藏状态的hmm优于那些具有更少状态的hmm，因为更大的模型具有更大的容量。总之，我们的方法证明了HMM可以比MM或NB模型更好地利用从墨水图案中提取的信息，因此是编码有用信息用于乐谱表示的最佳推理技术。
The performance of a model is dependent not only on the amount of knowledge available to the model but also on how the knowledge is exploited. We investigate the recognition of handwritten musical notation based on three related probabilistic inference techniques: Hidden Markov Models (HMMs), Markov Models (MMs) and Naïve Bayes (NBs). Music notes are written on a tablet. A sequence of ink patterns representing this symbol is captured and subsequently employed for constructing the models of HMMs, MMs and NBs. The proposed approach exploits both global and local information derived from ink patterns which we have demonstrated the exploitation of this information via different features employed in different HMMs. The specificity and sensitivity measures of these classification models are compared using unseen test datasets. The findings show that HMM outperformed MM and NB models, due to the ability of HMM in exploiting both transitional probability (transition matrix A) and the overall likelihood of the observed events (emission matrix B). Also, HMMs with more hidden states outperformed those with less states, since a larger model has more capacity. In conclusion, our approach demonstrated that HMM can better exploit information extracted from ink patterns than models of MM or NB, and therefore is an optimal inference technique to encoding useful information for musical notation representation.