Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-17829-0_8
Title: Semi-automatic Flickr group suggestion
Authors: Cai, J.
Zha, Z.-J. 
Tian, Q.
Wang, Z.
Keywords: Flickr Group
Group Suggestion
Semi-automatic
Issue Date: 2011
Source: Cai, J.,Zha, Z.-J.,Tian, Q.,Wang, Z. (2011). Semi-automatic Flickr group suggestion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6524 LNCS (PART 2) : 77-87. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-17829-0_8
Abstract: Flickr groups are self-organized communities to share photos and conversations with common interest and have gained massive popularity. Users in Flickr have to manually assign each image to the appropriated group. Manual assignment requires users to be familiar with existing images in each group and it is intractable and tedious. Therefore it prohibits users from exploiting the relevant groups. For solution to the problem, group suggestion has attracted increasing attention recently, which aims to suggest groups to user for a specific image. Existing works pose group suggestion as the automatic group prediction problem with a purpose of predicting the groups of each image automatically. Despite of dramatic progress in automatic group prediction, the prediction results are still not accurate enough. In this paper, we propose a semi-automatic group suggestion approach with Human-in-the-Loop. Given a user's image collection, we employ the pre-built group classifiers to predict the group of each image. These predictions are used as the initial group suggestions. We then select a small number of representative images from user's collection and ask user to assign the groups of them. Once obtaining user's feedbacks on the representative images, we infer the groups of remaining images through group propagation over multiple sparse graphs among the images. We conduct experiment on 15 Flickr groups with 127,500 images. The experimental results demonstrate the proposed framework is able to provide accurate group suggestions with quite a small amount of user effort. © 2011 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40695
ISBN: 3642178286
ISSN: 03029743
DOI: 10.1007/978-3-642-17829-0_8
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