In the current age of information technology, the issues of distributing
and utilizing images efficiently and effectively are of substantial
concern. Solutions to many of the problems arising from these issues are
provided by techniques of image processing, among which segmentation and
compression are topics of 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.
