Investigating Crowdsourcing as a Method to Collect Emotion Labels for Images

Olga Korovina Fabio Casati Radoslaw Nielek Marcos Baez Olga Berestneva

Labeling images is essential towards enabling the search and organization of digital media. This is true for both "factual", objective tags such as time, place and people, as well as for subjective labels, such as the emotion a picture generates. Indeed, the ability to associate emotions to images is one of the key functionality most image analysis services today strive to provide. In this paper we study how emotion labels for images can be crowdsourced and uncover limitations of the approach commonly used to gather training data today, that of harvesting images and tags from social media.