Utilizing the ubiquity of social media in modern society, the
emerging interdisciplinary field of social computing offers the
promise of important human-centered applications.
"Human-Centered Social Media Analytics" provides a timely and
unique survey of next-generation social computational
methodologies. The text explains the fundamentals of this field,
and describes state-of-the-art methods for inferring social status,
relationships, preferences, intentions, personalities, needs, and
lifestyles from human information in unconstrained visual data. The
collected chapters present a range of different viewpoints
examining the various possibilities and challenges to machine
understanding of humans in a social context.
Topics and features: includes perspectives from an international
and interdisciplinary selection of pre-eminent authorities;
presents balanced coverage of both detailed theoretical analysis
and real-world applications; examines social relationships in
human-centered media for the development of socially-aware video,
location-based, and multimedia applications; reviews techniques for
recognizing the social roles played by people in an event, and for
classifying human-object interaction activities; discusses the
prediction and recognition of human attributes via social media
analytics, including social relationships, facial age and beauty,
and occupation; requires no prior background knowledge of the
area.
This authoritative text/reference will be a valuable resource
for researchers and graduate students interested in social media
and networking, computer vision and biometrics, big data, and HCI.
Practitioners in these fields, as well as in image processing and
computer graphics, will also find the book of great interest.
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