Instagram hashtags as a source of semantic information for Automatic Image Annotation
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Date Issued
October 2021
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Advisor
Abstract
Billion digital images are uploaded every single day on the Internet and especially on social media. It is vital to develop effective and efficient methods that allow the retrieval of those images according to users' demands. Among the approaches that have been proposed for digital image retrieval is Automatic Image Annotation (AIA). AIA techniques automatically learn the visual representation of semantic concepts from a number of image samples, and use these concept models for tagging new images.
Learning good concept models requires representative pairs of image-tags. Manual annotation is a hard and time-consuming task since a large number of images are necessary to create effective concept models. Moreover, human judgment may contain errors and subjectivity. Therefore, it is highly desirable to find ways for automatically creating training examples, i.e., pairs of images and tags. Contemporary social media, such as Instagram, contain images and associated hashtags, providing a source of indirect annotation. Instagram is a photo-oriented social media platform where users upload images and describe them with hashtags; thus, it might be a rich source for automatically creating pairs of image-tags for AIA.
The thesis focuses on investigating Instagram images and hashtags as a field for AIA purposes. This primary research question is further analyzed through several studies: we define the portion of Instagram hashtags that are related to the visual content of images they accompany and we develop a methodology to locate stophashtags, i.e., common non-descriptive hashtags. We also employ the HITS algorithm in a crowdsourcing environment in order to filter Instagram hashtags and locate the ones that correspond to the visual content of Instagram images they accompany. Topic modelling of Instagram hashtags is introduced as a means for retrieving Instagram images in the traditional text-based information retrieval approach while transfer learning, utilizing filtered Instagram data (pairs of images and hashtags) is applied for a content-based image retrieval scenario.
Learning good concept models requires representative pairs of image-tags. Manual annotation is a hard and time-consuming task since a large number of images are necessary to create effective concept models. Moreover, human judgment may contain errors and subjectivity. Therefore, it is highly desirable to find ways for automatically creating training examples, i.e., pairs of images and tags. Contemporary social media, such as Instagram, contain images and associated hashtags, providing a source of indirect annotation. Instagram is a photo-oriented social media platform where users upload images and describe them with hashtags; thus, it might be a rich source for automatically creating pairs of image-tags for AIA.
The thesis focuses on investigating Instagram images and hashtags as a field for AIA purposes. This primary research question is further analyzed through several studies: we define the portion of Instagram hashtags that are related to the visual content of images they accompany and we develop a methodology to locate stophashtags, i.e., common non-descriptive hashtags. We also employ the HITS algorithm in a crowdsourcing environment in order to filter Instagram hashtags and locate the ones that correspond to the visual content of Instagram images they accompany. Topic modelling of Instagram hashtags is introduced as a means for retrieving Instagram images in the traditional text-based information retrieval approach while transfer learning, utilizing filtered Instagram data (pairs of images and hashtags) is applied for a content-based image retrieval scenario.
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Final Phd Stamatios Giannoulakis.pdf
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Abstract.pdf
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