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Machine Learning and Statistical Modeling Approaches to Image Retrieval (The Information Retrieval Series)

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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.

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