Name
Abstract | ||
|---|---|---|
Multiscale Bayesian approaches have attracted increasing attention for use in image segmentation. Generally, these methods tend to offer improved segmentation accuracy with reduced computational burden. Existing Bayesian segmentation methods use simple models of context designed to encourage large uniformly classified regions. Consequently, these context models have a limited ability to capture the complex contextual dependencies that are important in applications such as document segmentation. We propose a multiscale Bayesian segmentation algorithm which can effectively model complex aspects of both local and global contextual behavior. The model uses a Markov chain in scale to model the class labels that form the segmentation, but augments this Markov chain structure by incorporating tree based classifiers to model the transition probabilities between adjacent scales. The tree based classifier models complex transition rules with only a moderate number of parameters. One advantage to our segmentation algorithm is that it can be trained for specific segmentation applications by simply providing examples of images with their corresponding accurate segmentations. This makes the method flexible by allowing both the context and the image models to be adapted without modification of the basic algorithm. We illustrate the value of our approach with examples from document segmentation in which test, picture and background classes must be separated. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/83.913586 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
index terms—document segmentation,bayesian segmentation method,multiscale,classifier model,trainable context model,specific segmentation application,image segmentation,corresponding accurate segmentation,training,prior model,multiscale bayesian segmentation,multiscale bayesian segmentation algorithm,context model,wavelet.,improved segmentation accuracy,document segmentation,segmentation algorithm,bayesian approach,bayesian methods,context modeling,indexing terms,image classification,probability,transition probability,video compression,parameter estimation,markov chain,graphics,markov processes | Scale-space segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Context model,Artificial intelligence,Contextual image classification,Minimum spanning tree-based segmentation,Computer vision,Pattern recognition,Segmentation,Markov chain,Machine learning | Journal |
Volume | Issue | ISSN |
10 | 4 | 1057-7149 |
Citations | PageRank | References |
51 | 5.58 | 32 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H Cheng | 1 | 62 | 6.81 |
| Charles A. Bouman | 2 | 2740 | 473.62 |