Paper
17 March 2015 What do you think of my picture? Investigating factors of influence in profile images context perception
Author Affiliations +
Proceedings Volume 9394, Human Vision and Electronic Imaging XX; 93940D (2015) https://doi.org/10.1117/12.2082817
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Abstract
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 influence 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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
F. Mazza, M. P. Da Silva, P. Le Callet, and I. E. J. Heynderickx "What do you think of my picture? Investigating factors of influence in profile images context perception", Proc. SPIE 9394, Human Vision and Electronic Imaging XX, 93940D (17 March 2015); https://doi.org/10.1117/12.2082817
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Cited by 3 scholarly publications.
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KEYWORDS
Social networks

Computer vision technology

Machine vision

Visual process modeling

Analytical research

Databases

Error analysis

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