Bouchaffra Djamel
Mathematics and Computer Science Department, Grambling State University, Grambling, LA 71245, USA.
IEEE Trans Neural Netw. 2010 Apr;21(4):595-608. doi: 10.1109/TNN.2009.2039875. Epub 2010 Feb 17.
Hidden Markov models (HMMs) and their variants are capable to classify complex and structured objects. However, one of their major restrictions is their inability to cope with shape or conformation intrinsically: HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the visible observation (VO) sequence. In order to fulfill this crucial need, we propose a novel paradigm that we named conformation-based hidden Markov models (COHMMs). This new formalism classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean vector space. This is accomplished by modeling the noise contained in the shape composed by the VO sequence. We cover the one-level as well as the multilevel COHMMs. Five problems are assigned to a multilevel COHMM: 1) sequence probability evaluation, 2) statistical decoding, 3) structural decoding, 4) shape decoding, and 5) learning. We have applied the COHMMs formalism to human face identification tested on different benchmarked face databases. The results show that the multilevel COHMMs outperform the embedded HMMs as well as some standard HMM-based models.
隐马尔可夫模型（HMMs）及其变体能够对复杂的结构化对象进行分类。然而，它们的主要限制之一是本质上无法处理形状或构象：基于HMM的技术难以预测由可见观测（VO）序列的符号所形成的n维形状。为了满足这一关键需求，我们提出了一种新颖的范式，我们将其命名为基于构象的隐马尔可夫模型（COHMMs）。这种新的形式体系通过将HMM状态转移图的节点嵌入欧几里得向量空间来对VO序列进行分类。这是通过对由VO序列组成的形状中包含的噪声进行建模来实现的。我们涵盖了单级以及多级COHMMs。有五个问题被分配给多级COHMM：1）序列概率评估，2）统计解码，3）结构解码，4）形状解码，以及5）学习。我们已将COHMMs形式体系应用于在不同基准人脸数据库上进行测试的人脸识别。结果表明，多级COHMMs优于嵌入式HMMs以及一些基于标准HMM的模型。
