Machine Learning and Statistical Modeling Approaches to Image Retrieval

• Κύριοι συγγραφείς: Chen, Yixin (Συγγραφέας), Li, Jia (Συγγραφέας), Wang, James Z. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2004.
• Σειρά: The Information Retrieval Series, 14
• Θέματα: Physics.; Multimedia information systems.; Artificial intelligence.; Image processing.; Management information systems.; Computer science.; Optics.; Optoelectronics.; Plasmons (Physics).; Statistics.; Optics, Optoelectronics, Plasmonics and Optical Devices.; Multimedia Information Systems.; Artificial Intelligence (incl. Robotics).; Management of Computing and Information Systems.; Statistics, general.; Image Processing and Computer Vision.

 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. 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, culture and entertainment. Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well. 

 Image Retrieval and Linguistic Indexing -- Machine Learning and Statistical Modeling -- A Robust Region-Based Similarity Measure -- Cluster-Based Retrieval by Unsupervised Learning -- Categorization by Learning and Reasoning with Regions -- Automatic Linguistic Indexing of Pictures -- Modeling Ancient Paintings -- Conclusions and Future Work.