Using Group Prior to Identify People in Consumer Images

By Andrew C. Gallagher, Tsuhan Chen. 2007

57 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Various other cues have been explored, for example: attributes classification [40, 41], social context [42, 43], relative camera positions [44], space-time priors [45], and photo-album priors [46].”
Person Recognition in Personal Photo Collections (2015, methodology): “One can also exploit album context, which is a rich source of identity information [16, 34, 35]; however, it was not used in this work for fair comparison.”
A data-driven approach to cleaning large face datasets (2014, methodology): “One of our goals is for the QP to label faces that are likely false positives from the face detector as outliers.”
Autotagging Facebook: Social network context improves photo annotation (2008, background): “Other efforts have worked with ambiguously labeled data: [3], [4], and [7] resolved labels in photo collections using context from captions.”
Understanding images of groups of people (2009, background): “In [10, 19, 24], co-occurences between individuals in labeled images are considered for reasoning about the identities of groups of people (instead of one person at a time).”
Joint People, Event, and Location Recognition in Personal Photo Collections Using Cross-Domain Context (2010, background): “We also note that previous work that used an MRF to capture exclusivity and the group prior (e.g. [8]) is essentially the special case of our framework where only the people-people relation is used.”
Learning from ambiguously labeled images (2009, methodology): “Gallagher and Chen [9] address the similar problem of retrieval from consumer photo collections, in which several people appear in each image which is labeled with their names.”
Where's Waldo: Matching people in images of crowds (2011, background): “We also note that prior authors have explored color models for matching people [7, 11, 13], co-occurrence cues [6], and other contextual cues [8, 13] in other settings.”