Identifying Professional Photographers Through Image Quality and Aesthetics in Flickr
Sofia Strukova, Rub\'en Gaspar Marco, Jos\'e A. Ruip\'erez-Valiente,, F\'elix G\'omez M\'armol

TL;DR
This study introduces a large labeled Flickr dataset to classify professional photographers using multimodal features, analyzing photo quality, aesthetics, and social activity to distinguish expertise levels.
Contribution
It creates and open-sources one of the largest Flickr datasets with multimodal data and demonstrates the feasibility of predicting professional status based on various features.
Findings
Feasibility of predicting professional photographers using machine learning.
Correlation between photo aesthetics, technical quality, and social activity.
Characteristics that differentiate professionals from amateurs.
Abstract
In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users' interaction which can be used to perform a data-driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self-reported occupation labels and several feature representations out of the user, photo and crowdsourced…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Aesthetic Perception and Analysis · Image Retrieval and Classification Techniques
