What do you think of my picture? investigating factors of inuence in profile images context perception

F. Mazza, M.P. Da Silva, P. Le Callet, I.E.J. Heynderickx

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)


Multimedia quality assessment has been an important research topic during the last decades. The original focus on artifact visibility has been extended during the years to aspects as image aesthetics, interestingness and memorability. More recently, Fedorovskaya proposed the concept of 'image psychology': this concept focuses on additional quality dimensions related to human content processing. While these additional dimensions are very valuable in understanding preferences, it is very hard to define, isolate and measure their effect on quality. In this paper we continue our research on face pictures investigating which image factors inuence context perception. We collected perceived fit of a set of images to various content categories. These categories were selected based on current typologies in social networks. Logistic regression was adopted to model category fit based on images features. In this model we used both low level and high level features, the latter focusing on complex features related to image content. In order to extract these high level features, we relied on crowdsourcing, since computer vision algorithms are not yet sufficiently accurate for the features we needed. Our results underline the importance of some high level content features, e.g. the dress of the portrayed person and scene setting, in categorizing image.

Original languageEnglish
Title of host publicationHuman Vision and Electronic Imaging XX, 8 February 2015, San Francisco, California
Place of PublicationBellingham
ISBN (Electronic)9781628414844
Publication statusPublished - 2015
EventHuman Vision and Electronic Imaging XX - San Francisco, United States
Duration: 9 Feb 201512 Feb 2015

Publication series

NameProceedings of SPIE


ConferenceHuman Vision and Electronic Imaging XX
Country/TerritoryUnited States
CitySan Francisco


  • Content perception
  • Crowdsourcing
  • High level features
  • Portrait images
  • Social bias
  • Social networks


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