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, such as the emotion. 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.
Investigating crowdsourcing as a method to collect emotion labels for images / Korovina, Olga; Casati, Fabio; Baez, Marcos; Berestneva, Olga; Nielek, Radoslaw. - 2018-:(2018), pp. 1-6. ( 2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018 can 2018) [10.1145/3170427.3188667].
Investigating crowdsourcing as a method to collect emotion labels for images
Korovina, Olga;Casati, Fabio;Baez, Marcos;
2018-01-01
Abstract
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, such as the emotion. 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
