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Title: QUOTE: “Querying” Users as Oracles in Tag Engines a Semi-Supervised Learning Approach to Personalized Image Tagging
Authors: Nwana, Amandianeze O
Chen, Tsuhan
Keywords: cs.IR
Issue Date: Dec-2016
Publisher: IEEE
Citation: Nwana, Amandianeze O, Chen, Tsuhan (2016-12). QUOTE: “Querying” Users as Oracles in Tag Engines a Semi-Supervised Learning Approach to Personalized Image Tagging. 2016 IEEE International Symposium on Multimedia (ISM) abs/1601.06440. ScholarBank@NUS Repository.
Abstract: One common trend in image tagging research is to focus on visually relevant tags, and this tends to ignore the personal and social aspect of tags, especially on photoblogging websites such as Flickr. Previous work has correctly identified that many of the tags that users provide on images are not visually relevant (i.e. representative of the salient content in the image) and they go on to treat such tags as noise, ignoring that the users chose to provide those tags over others that could have been more visually relevant. Another common assumption about user generated tags for images is that the order of these tags provides no useful information for the prediction of tags on future images. This assumption also tends to define usefulness in terms of what is visually relevant to the image. For general tagging or labeling applications that focus on providing visual information about image content, these assumptions are reasonable, but when considering personalized image tagging applications, these assumptions are at best too rigid, ignoring user choice and preferences. We challenge the aforementioned assumptions, and provide a machine learning approach to the problem of personalized image tagging with the following contributions: 1.) We reformulate the personalized image tagging problem as a search/retrieval ranking problem, 2.) We leverage the order of tags, which does not always reflect visual relevance, provided by the user in the past as a cue to their tag preferences, similar to click data, 3.) We propose a technique to augment sparse user tag data (semi-supervision), and 4.) We demonstrate the efficacy of our method on a subset of Flickr images, showing improvement over previous state-of-art methods.
Source Title: 2016 IEEE International Symposium on Multimedia (ISM)
DOI: 10.1109/ism.2016.0016
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