Instagram Hashtags as Image Annotation Metadata

Abstract : Image tagging is an essential step for developing automatic image annotation methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work we investigate whether tags accompanying photos in social media and especially the Instagram hashtags, provide a form of image annotation. If such a claim is proved then Instagram could be a very rich source of training data, easily collectable automatically, for the development of automatic image annotation techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner / creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose a set of 30 randomly chosen, from Instagram, images were used as a dataset for our research. Then, one to four hashtags, considered as the most descriptive ones for the image in question, were selected among the hashtags used by the image owner. Three online questionnaires with ten images each were distributed to experiment participants in order to choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 55% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed.
Type de document :
Communication dans un congrès
Richard Chbeir; Yannis Manolopoulos; Ilias Maglogiannis; Reda Alhajj. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. IFIP Advances in Information and Communication Technology, AICT-458, pp.206-220, 2015, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-23868-5_15〉
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01385356
Contributeur : Hal Ifip <>
Soumis le : vendredi 21 octobre 2016 - 11:41:16
Dernière modification le : vendredi 1 décembre 2017 - 01:16:44

Fichier

978-3-319-23868-5_15_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Stamatios Giannoulakis, Nicolas Tsapatsoulis. Instagram Hashtags as Image Annotation Metadata. Richard Chbeir; Yannis Manolopoulos; Ilias Maglogiannis; Reda Alhajj. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. IFIP Advances in Information and Communication Technology, AICT-458, pp.206-220, 2015, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-23868-5_15〉. 〈hal-01385356〉

Partager

Métriques

Consultations de la notice

124

Téléchargements de fichiers

64