基于隐Markov模型的图像方位识别

Image Orientation Recognition Based on Hidden Markov Model

提出一种基于隐Markov模型(Hidden Markov Model,HMM)的图像方位识别方法.将待识别的目标图像进行分割,对子图像进行奇异值分解,提取奇异值向量形成观测序列,即图像奇异值向量作为HMM的观测向量.确定HMM参数并计算其最大似然概率,按待识别图像最大似然概率对应所属的聚类进行识别.实验结果表明,3类共150幅目标图像的识别率达到了85%.

A method of image orientation recognition is put forward, based on HMM （Hidden Markov Model）. Segmenting the target image to recognize into sub-images, a singular value decomposition is conducted for them to extract singular value vectors so as to form an observation sequence, i.e., the singular value vectors of the image are taken as HMM observation vectors. Then, the HMM parameters are determined with their maximum likelihood calculated, and the images are recognized according to the clustering which the maximum likelihood of an image to recognize corresponds to. Test results showed that the recognition rate of 150 target images in 3 clusters is up to 85%.