NDLI: Automatic annotation of weakly-tagged social images on flickr using latent topic discovery of multiple groups

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Automatic annotation of weakly-tagged social images on flickr using latent topic discovery of multiple groups

Automatic annotation of weakly-tagged social images on flickr using latent topic discovery of multiple groups

Content Provider	ACM Digital Library
Author	Zhu, Ke Shao, Jian Chen, Ye
Abstract	At photo-sharing websites like Flickr, a lot of "weakly-tagged" social images can not be effectively retrieved because they are noisily or sparsely tagged. Since users in Flickr often recommend their uploaded images to multiple associated groups according to the latent topics in each image, we propose a novel two-stage approach to automatically annotate these weakly-tagged social images. In the first stage called as topic-guided tag refinement, the latent topics in each group are beforehand discovered by the Latent Dirichlet Allocation model, then those noisy tags are filtered in group level and topic-relevant tags are re-ranked in image level before and after tag prorogation among similar images respectively. In the second stage called as hierarchical multi-group tag fusion, the hierarchical topic structure among multiple groups is beforehand discovered by WordNet, and is then used to fuse the generated tags from multiple groups in hierarchical way. Experiments on 93481 images from three Flickr groups show the effectiveness of our proposed approach.
Starting Page	83
Ending Page	88
Page Count	6
File Format	PDF
ISBN	9781605587608
DOI	10.1145/1631005.1631023
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-10-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Automatic social image annotation Topic-guided tag refinement Latent dirichlet allocation Hierarchical multi-group fusion
Content Type	Text
Resource Type	Article

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