000 04376nam a22005295i 4500
001 978-3-319-05491-9
003 DE-He213
005 20200421111154.0
007 cr nn 008mamaa
008 140324s2014 gw | s |||| 0|eng d
020 _a9783319054919
_9978-3-319-05491-9
024 7 _a10.1007/978-3-319-05491-9
_2doi
050 4 _aQH323.5
072 7 _aUYQP
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
082 0 4 _a570.15195
_223
245 1 0 _aHuman-Centered Social Media Analytics
_h[electronic resource] /
_cedited by Yun Fu.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aVIII, 208 p. 97 illus., 51 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart I: Social Relationships in Human-Centered Media -- Bridging Human-Centered Social Media Content across Web Domains -- Learning Social Relations from Videos -- Community Understanding in Location-Based Social Networks -- Social Role Recognition for Human Event Understanding -- Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities -- Part II: Human Attributes in Social Media Analytics -- Recognizing People in Social Context -- Female Facial Beauty Attribute Recognition and Editing -- Facial Age Estimation -- Identity and Kinship Relations in Group Pictures -- Recognizing Occupations through Probabilistic Models.
520 _aUtilizing 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. Dr. Yun Fu is an assistant professor in the Department of Electrical and Computer Engineering at Northeastern University, Boston, MA, USA, where he is the founder of the Synergetic Media Learning (SMILE) Lab.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aUser interfaces (Computer systems).
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aBiometrics (Biology).
650 1 4 _aComputer Science.
650 2 4 _aBiometrics.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
700 1 _aFu, Yun.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319054902
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-05491-9
912 _aZDB-2-SCS
942 _cEBK
999 _c53447
_d53447