一种基于二维隐马尔可夫模型的图像分类算法  

Algorithm of image classification based on two-dimensional hidden Markov model

作　　者：胡迎松[1] 朱阿柯[1] 陈刚[2] 陈中新[3] 

机构地区：[1]华中科技大学计算机科学与技术学院,湖北武汉430074 [2]武汉科技大学计算机科学与技术学院,湖北武汉430081 [3]湖北清江水电开发有限责任公司,湖北宜昌443002

出　　处：《计算机应用》2005年第4期760-762,共3页journal of Computer Applications

摘　　要：针对图像分块之间的相互依赖关系,提出一种基于二维隐马尔可夫模型的图像分类算 法。该算法将一维隐马尔可夫模型扩展成二维隐马尔可夫模型,模型中相邻的图像分块在平面两个 方向上按条件转移概率进行状态转换,反应出两个维上的依赖关系。隐马尔可夫模型参数通过期望 最大化算法(EM)来估计。同时,本文利用二维Viterbi算法,在训练隐马尔可夫模型的基础上,实现 对图像进行最优分类。文件图像分割的应用表明,隐马尔可夫算法优于CART算法。Aimed at the inter-block dependency, an image classification algorithm based on a two hidden Markov model(2DHMM) extension from the one dimensional HMM was developed. The 2DHMM has transition probabilities conditioned on the states of neighboring blocks from both directions. Thus, the dependency in two dimensions can be reflected simultaneously. The HMM parameters were estimated by the EM algorithm. A two dimensional version of the Viterbi algorithm was also developed to classify optimally an image based on the trained HMM. Application of the HMM algorithm to document image shows that the algorithm performs better than CART.

关 键 词：二维隐马尔可夫模型 图像分类 EM算法 VITERBI算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]