Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8962
Title: Instagram Hashtags as Image Annotation Metadata
Authors: Giannoulakis, Stamatios 
Tsapatsoulis, Nicolas 
Keywords: Instagram
Hashtags
Image tagging
Image retrieval
Machine learning
Issue Date: 2015
Publisher: Springer, Cham
Source: Artificial Intelligence Applications and Innovations : 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14-17, 2015, Proceedings, Pages 206-220
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.
URI: http://ktisis.cut.ac.cy/handle/10488/8962
ISBN: Print ISBN 978-3-319-23867-8
Online ISBN 978-3-319-23868-5
Rights: © 2016 Springer International Publishing AG. Part of Springer Nature.
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

Show full item record

Page view(s) 5

24
checked on Mar 24, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.