Scalable hypergraph-based image retrieval and tagging system
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...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2018
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/4119 https://doi.org/10.1109/ICDE.2018.00032 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-5122 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-51222023-08-04T01:23:49Z Scalable hypergraph-based image retrieval and tagging system CHEN, Lu GAO, Yunjun ZHANG, Yuanliang WANG, Sibo ZHENG, Baihua 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. 2018-04-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/4119 info:doi/10.1109/ICDE.2018.00032 https://doi.org/10.1109/ICDE.2018.00032 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Image retrieval image tagging hypergraph Databases and Information Systems |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Image retrieval image tagging hypergraph Databases and Information Systems |
spellingShingle |
Image retrieval image tagging hypergraph Databases and Information Systems CHEN, Lu GAO, Yunjun ZHANG, Yuanliang WANG, Sibo ZHENG, Baihua Scalable hypergraph-based image retrieval and tagging system |
description |
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. |
format |
text |
author |
CHEN, Lu GAO, Yunjun ZHANG, Yuanliang WANG, Sibo ZHENG, Baihua |
author_facet |
CHEN, Lu GAO, Yunjun ZHANG, Yuanliang WANG, Sibo ZHENG, Baihua |
author_sort |
CHEN, Lu |
title |
Scalable hypergraph-based image retrieval and tagging system |
title_short |
Scalable hypergraph-based image retrieval and tagging system |
title_full |
Scalable hypergraph-based image retrieval and tagging system |
title_fullStr |
Scalable hypergraph-based image retrieval and tagging system |
title_full_unstemmed |
Scalable hypergraph-based image retrieval and tagging system |
title_sort |
scalable hypergraph-based image retrieval and tagging system |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2018 |
url |
https://ink.library.smu.edu.sg/sis_research/4119 https://doi.org/10.1109/ICDE.2018.00032 |
_version_ |
1773551426819063808 |