This book offers a systematic introduction to an understanding-oriented approach to multimedia content analysis. It integrates the visual understanding and learning models into a unified framework, within which the visual understanding guides the model learning while the learned models improve the visual understanding. More specifically, it discusses multimedia content representations and analysis including feature selection, feature extraction, image tagging, user-oriented tag recommendation and understanding-oriented multimedia applications. The book was nominated by the University of Chinese Academy of Sciences and China Computer Federation as an outstanding PhD thesis. By providing the fundamental technologies and state-of-the-art methods, it is a valuable resource for graduate students and researchers working in the field computer vision and machine learning.

Nominated by the University of Chinese Academy of Sciences and China Computer Federation as an outstanding PhD thesis Proposes an novel understanding-oriented approach for multimedia content analysis Includes both feature representations of the multimedia content and various learning approaches for content understanding Includes supplementary material: sn.pub/extras

Autorentext
Zechao Li is an associate professor at the School of Computer Science, Nanjing University of Science and Technology, China. He received his B.E. degree from the University of Science and Technology of China (USTC), Anhui Province, China, in 2008, and his Ph.D degree in Pattern Recognition and Intelligent Systems from the National Laboratory of Pattern Recognition, Institute of Automation, the Chinese Academy of Sciences in 2013. His research interests include machine learning, subspace learning and multimedia understanding. He is the recipient of the 2015 Excellent Doctoral Dissertation from the Chinese Academy of Sciences, the 2015 Excellent Doctoral Dissertation from the China Computer Federation and the 2013 Chinese Academy of Science President Scholarship. He also received the Top 10% Paper Award at MMSP 2015.

Inhalt
Introduction.- Understanding-oriented Unsupervised Feature Selection.- Understanding-oriented Feature.- Personalized Tag Recommendation.- Understanding-oriented Multimedia News Retrieval.- Understanding-oriented Multimedia News Summarization.- Conclusion.