Toward an estimation of user tagging credibility for social image retrieval
Author
Ginsca, A. L.
Popescu, A.
Ionescu, B.
Armağan, Anıl
Kanellos, I.
Date
2014-11Source Title
MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
Publisher
ACM
Pages
1021 - 1024
Language
English
Type
Conference PaperItem Usage Stats
160
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144
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Abstract
Existing image retrieval systems exploit textual or/and visual information to return results. Retrieval is mostly focused on data themselves and disregards the data sources. In Web 2.0 platforms, the quality of annotations provided by different users can vary strongly. To account for this variability, we complement existing methods by introducing user tagging credibility in the retrieval process. Tagging credibility is automatically estimated by leveraging a large set of visual concept classifiers learned with Overfeat, a convolutional neural network (CNN) feature. A good image retrieval system should return results that are both relevant and diversified and here we tackle both challenges. Classically, we diversify results by using a k-Means algorithm and increase relevance by favoring images uploaded by users with good credibility estimates. Evaluation is performed on DIV400, a publicly available social image retrieval dataset and shows that our method is competitive with existing approaches.
Keywords
Image retrievalNeural networks
User interfaces
Convolutional neural network
Data-sources
Image retrieval systems
k-Means algorithm
Retrieval process
Social image retrievals
Visual concept
Visual information
Search engines
Permalink
http://hdl.handle.net/11693/27741Published Version (Please cite this version)
https://doi.org/10.1145/2647868.2655033Collections
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