Show simple item record

dc.contributor.authorBrenner, Markus
dc.date.accessioned2015-07-20T12:27:23Z
dc.date.available2015-07-20T12:27:23Z
dc.date.issued2014-09-29
dc.identifier.citationBrenner, M, 2014. Context-based Semi-supervised Joint People Recognition in Consumer Photo Collections using Markov Networks. Queen Mary University of Londonen_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/7931
dc.descriptionThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authoren_US
dc.description.abstractFaces, along with the personal identities behind them, are effective elements in organizing a collection of consumer photos, as they represent who was involved. However, the accurate discrimination and subsequent recognition of face appearances is still very challenging. This can be attributed to the fact that faces are usually neither perfectly lit nor captured, particularly in the uncontrolled environments of consumer photos. Unlike, for instance, passport photos that only show faces stripped of their surroundings, Consumer Photo Collections contain a vast amount of meaningful context. For example, consecutively shot photos often correlate in time, location or scene. Further information can also be provided by the people appearing in photos, such as their demographics (ages and gender are often easier to surmise than identities), clothing, or the social relationships among co-occurring people. Motivated by this ubiquitous context, we propose and research people recognition approaches that consider contextual information within photos, as well as across entire photo collections. Our aim of leveraging additional contextual information (as opposed to only considering faces) is to improve recognition performance. However, instead of requiring users to explicitly label specific pieces of contextual information, we wish to implicitly learn and draw from the seemingly coherent content that exists inherently across an entire photo collection. Moreover, unlike conventional approaches that usually predict the identity of only one person’s appearance at a time, we lay out a semi-supervised approach to jointly recognize multiple peoples’ appearances across an entire photo collection simultaneously. As such, our aim is to find the overall best recognition solution. To make context-based joint recognition of people feasible, we research a sparse but efficient graph-based approach that builds on Markov Networks and utilizes distance-based face description methods. We show how to exploit the following specific contextual cues: time, social semantics, body appearances (clothing), gender, scene and ambiguous captions. We also show how to leverage crowd-sourced gamified feedback to iteratively improve recognition performance. Experiments on several datasets demonstrate and validate the effectiveness of our semisupervised graph-based recognition approach compared to conventional approaches.en_US
dc.description.sponsorshipThis work is partially supported by European Union research project CUbRIK under grant agreement FP7-287704.en_US
dc.language.isoenen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectfacial recognitionen_US
dc.subjectperformanceen_US
dc.subjectcontext-based recognitionen_US
dc.subjectMarkov Networksen_US
dc.subjectGamified Ambiguous Feedbacken_US
dc.subjectFace Detectionen_US
dc.titleContext-based Semi-supervised Joint People Recognition in Consumer Photo Collections using Markov Networksen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Theses [3321]
    Theses Awarded by Queen Mary University of London

Show simple item record