Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 571)
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Table of contents (8 chapters)
Keywords
About this book
Image segmentation is a process for dividing an image into its constituent parts. For block-based segmentation using statistical classification, an image is divided into blocks and a feature vector is formed for each block by grouping statistics of its pixel intensities. Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors.
Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally.
The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization.
Image Segmentation and Compression Using Hidden Markov Models is an essential reference source for researchers and engineers working in statistical signal processing or image processing, especially those who are interested in hidden Markov models. It is also of value to those working on statistical modeling.
Authors and Affiliations
Bibliographic Information
Book Title: Image Segmentation and Compression Using Hidden Markov Models
Authors: Jia Li, Robert M. Gray
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-4497-5
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2000
Hardcover ISBN: 978-0-7923-7899-0Published: 31 August 2000
Softcover ISBN: 978-1-4613-7027-7Published: 03 October 2012
eBook ISBN: 978-1-4615-4497-5Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XIII, 141
Topics: Image Processing and Computer Vision, Signal, Image and Speech Processing, Electrical Engineering, Computer Graphics, Management of Computing and Information Systems