Scalable hypergraph-based image retrieval and tagging system

Publication Type

Conference Proceeding Article

Publication Date

4-2018

Abstract

Massive amounts of images textually annotated by different users are provided by social image websites, e.g., Flickr. Social images are always associated with various information, such as visual features, tags, and users. In this paper, we utilize hypergraph instead of ordinary graph to model social images, since relations among various information are more sophisticated than pairwise. Based on the hypergraph, we propose HIRT, a scalable image retrieval and tagging system, which uses Personalized PageRank to measure vertex similarity, and employs top-k search to support image retrieval and tagging. To achieve good scalability and efficiency, we develop parallel and approximate top-k search algorithms with quality guarantees. Experiments on a large Flickr dataset confirm the effectiveness and efficiency of our proposed system HIRT compared with existing state-of-the-art hypergraph based image retrieval system. In addition, our parallel and approximate top-k search methods are verified to be more efficient than the state-of-the-art methods and meanwhile achieve higher result quality.

Keywords

Image retrieval, image tagging, hypergraph

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

2018 34th IEEE International Conference on Data Engineering (ICDE): Paris, April 16-19: Proceedings

First Page

257

Last Page

268

ISBN

9781538655207

Identifier

10.1109/ICDE.2018.00032

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/ICDE.2018.00032

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