加权Contourlet域隐Markov树纹理图像特征提取模型

Texture image feature extraction model based on weighted Contourlet domain Hidden Markov Tree

为更好地利用相关性描述纹理图像特征,针对目前Contourlet域隐马尔可夫树模型（CHMT）只考虑父结点的一个相邻结点对子结点影响的不足,提出一种加权Contourlet域隐马尔可夫树模型对纹理图像特征提取模型。在分析子结点的状态时,考虑父结点信息的同时利用权重评价父结点兄弟结点对子结点的影响,并通过附加状态转移矩阵体现出来,更加准确地描述了Contourlet系数和HMT的内在联系;运用K-L距离计算图像间的相似度。实验结果表明,改进的模型比CHMT平均检索率高出7%~46%。

In order to describe the texture feature using correlation excellently,a texture feature extraction model according to the weighted Contourlet domain Hidden Markov Tree（CHMT） model is presentedi,n view of the deficiency of current CHMT model which is just considered the affection of one adjacent node with its child node.This model is considered the information of parent node,as well as the affection of parent node with child node using weight is evaluated,which is re-flected through the additional state transition matrix when analyzes the sub nodes’ statuss,o that the internal relation of Con-tourlet coefficient with HMT is described more accurately.At the same timet,he similarity is calculated by using K-L dis-tance.The experimental results show that this model is better than the CHMT average retrieval rate of 7%~46%.