Joint Hypergraph Learning for Tag-Based Image Retrieval

As the image sharing websites like Flickr become more and more popular, extensive scholars concentrate on tag-based image retrieval. It is one of the important ways to find images contributed by social users. In this research field, tag information and diverse visual features have been investigated....

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Bibliographic Details
Published in:IEEE transactions on image processing 2018-09, Vol.27 (9), p.4437-4451
Main Authors: Wang, Yaxiong, Zhu, Li, Qian, Xueming, Han, Junwei
Format: Article
Language:English
Subjects:
feature fusion
hypergraph
Image edge detection
Image retrieval
pseudo relevance feedback
Semantics
Social network services
Tag-based image retrieval
Task analysis
visual feature
Visualization
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Summary:As the image sharing websites like Flickr become more and more popular, extensive scholars concentrate on tag-based image retrieval. It is one of the important ways to find images contributed by social users. In this research field, tag information and diverse visual features have been investigated. However, most existing methods use these visual features separately or sequentially. In this paper, we propose a global and local visual features fusion approach to learn the relevance of images by hypergraph approach. A hypergraph is constructed first by utilizing global, local visual features, and tag information. Then, we propose a pseudo-relevance feedback mechanism to obtain the pseudo-positive images. Finally, with the hypergraph and pseudo relevance feedback, we adopt the hypergraph learning algorithm to calculate the relevance score of each image to the query. Experimental results demonstrate the effectiveness of the proposed approach.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2018.2837219