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 recentlydeveloped
machine learning and statistical modeling methods for image
indexing and retrieval. Some results are quite promising.