VIP: Finding Important People in Images

dc.contributor.authorMathialagan, Clint Solomonen
dc.contributor.committeechairBatra, Dhruven
dc.contributor.committeememberAbbott, A. Lynnen
dc.contributor.committeememberParikh, Devien
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2015-06-26T08:00:50Zen
dc.date.available2015-06-26T08:00:50Zen
dc.date.issued2015-06-25en
dc.description.abstractPeople preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context of the event. This work analyzes the concept of the importance of individuals in group photographs. We address two specific questions - Given an image, who are the most important individuals in it? Given multiple images of a person, which image depicts the person in the most important role? We introduce a measure of importance of people in images and investigate the correlation between importance and visual saliency. We find that not only can we automatically predict the importance of people from purely visual cues, incorporating this predicted importance results in significant improvement in applications such as im2text (generating sentences that describe images of groups of people).en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:5709en
dc.identifier.urihttp://hdl.handle.net/10919/53706en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectComputer Visionen
dc.subjectMachine Learningen
dc.subjectImportanceen
dc.titleVIP: Finding Important People in Imagesen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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