Paper Info
Title
Multiscale segmentation for MRC document compression using a Markov random field model
Abstract
The Mixed Raster Content (MRC) standard (ITU-T T.44) specifies a framework for document compression which can dramatically improve the compression/quality tradeoff as compared to traditional lossy image compression algorithms. The key to MRC's performance is the separation of the document into foreground and background layers, represented as a binary mask. In this paper, we propose a novel multiscale segmentation scheme based on the sequential application of two algorithms. The first algorithm, Cost Optimized Segmentation (COS), is a blockwise segmentation algorithm formulated in a global cost optimization framework. The second algorithm, Connected Component Classification (CCC), refines the initial segmentation by classifying feature vectors of connected components using a Markov random field (MRF) model. The combined COS/CCC segmentation algorithms are then incorporated into a multiscale framework in order to improve the segmentation accuracy of text with varying size.
Year
DOI
Venue
2010
10.1109/ICASSP.2010.5495328
Acoustics Speech and Signal Processing
Keywords
Field
DocType
Markov processes,data compression,image coding,image segmentation,text analysis,MRC document compression,Markov random field model,blockwise segmentation algorithm,connected component classification,cost optimized segmentation,image compression algorithm,mixed Raster content standard,multiscale segmentation,Image segmentation,MRC compression,Markov random field,multiscale image analysis
Mixed raster content,Scale-space segmentation,Pattern recognition,Segmentation,Markov random field,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Data compression,Hidden Markov model
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4244-4296-6
978-1-4244-4296-6
1
PageRank 
References 
Authors
0.38
7
2
Name
Order
Citations
PageRank
Haneda, E.110.38
Charles A. Bouman22740473.62