Machine Learning and Statistical Modeling Approaches
to Image Retrieval
Yixin Chen
University of Mississippi
Jia Li
The Pennsylvania State University
James Z. Wang
The Pennsylvania State University
Published by Kluwer
Academic Publishers
Read a book review published in ACM Computing Reviews.
Abstract:
In the early 1990s, the establishment of the Internet brought forth a
revolutionary viewpoint of information storage, distribution, and
processing: the World-Wide Web is becoming an enormous and expanding
distributed digital library. Along with the development of the Web,
image indexing and retrieval have grown into research areas sharing a
vision of intelligent agents: computer programs capable of making
``meaningful interpretations'' of images based on automatically
extracted imagery features. Far beyond Web searching, image indexing
and retrieval can potentially be applied to many other areas,
including biomedicine, space science, biometric identification,
digital libraries, the military, education, commerce, cultural, and
entertainment.
Although much research effort has been put into image indexing and
retrieval, we are still very far from having computer programs with
even the modest level of human intelligence. Decades of research have
shown that designing a generic computer algorithm for object
recognition, scene understanding, and automatically translating the
content of images to linguistic terms is a highly challenging
task. However, a series of successes have been achieved in recognizing
a relatively small set of objects or concepts within specific domains
based on learning and statistical modeling techniques. This motivates
many researchers to use recently-developed machine learning and
statistical modeling methods for image indexing and retrieval. Some
results are quite promising.
The topics of this book reflect our personal biases and experiences of
machine learning and statistical modeling based image indexing and
retrieval. A significant portion of the book is built upon material
from articles we have written, our unpublished reports, and talks we
have presented at several conferences and workshops. In particular,
the book presents five different techniques of integrating machine
learning and statistical modeling into image indexing and retrieval
systems: an similarity measure defined over region-based image
features; an image clustering and retrieval scheme based on dynamic
graph partitioning; an image categorization method based on the
information of regions contained in the images; modeling semantic
concepts of photographic images by stochastic processes; and the
characterization of ancient paintings using a mixture of stochastic
models. The first two techniques are within the scope of image
retrieval. The remaining three techniques are closely related to
automatic linguistic image indexing.
The book will be of value to faculty seeking a textbook that covers
some of the most recent advances in the areas of automated image
indexing, retrieval, and annotation. Researchers and graduate
students interested in exploring state-of-the-art research in the
related areas will find in-depth treatments of the covered topics.
Demonstrations of some of the techniques presented in the book are
available at http://riemann.ist.psu.edu.
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Last Modified:
January 28, 2004
© 2004