Bayesian Document Segmentation Based on Complex Wavelet Domain Hidden Markov Tree Models

摘要：

A texture-based Bayesian document segmentation method is investigated in this paper.This Bayesian method is used to fuse texture likelihood and prior contextual knowledge to achieve document segmentation.The texture likelihood is based on a complex wavelet domain hidden Markov tree (HMT)model and the prior contextual is based on a hybrid tree model.A redundant wavelet domain Gaussian mixture model is employed to capture pixel-level features in the HMT model.Several document images are segmented to verify the proposed method. Comparisons with other corresponding models are made.

关键词： Document segmentation Complex wavelet transform Hidden Markov tree model.

作者: Junxi Sun Dongbing Gu Hua Cai Guangwen Liu Guangqiu Chen

作者单位: Department of Electronic and Information Engineering Changchun University of Science and Technology Department of Computing and Electronic Systems,University of Essex Colchester,CO4 3SQ,UK

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 493-498

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）