Manifold-Ranking-Based Keyword Propagation for Image Retrieval
<p/> <p>A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straightforward manner by exploring...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2006-01-01
|
Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/ASP/2006/79412 |
id |
doaj-e51b8d49b7c442bbae009de587c6f3a8 |
---|---|
record_format |
Article |
spelling |
doaj-e51b8d49b7c442bbae009de587c6f3a82020-11-25T00:19:21ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802006-01-0120061079412Manifold-Ranking-Based Keyword Propagation for Image RetrievalLi MingjingMa Wei-YingZhang Hong-JiangTong HanghangHe JingruiZhang Changshui<p/> <p>A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straightforward manner by exploring the relationship among all images in the feature space in the learning stage. In relevance feedback, the feedback information can be naturally incorporated to refine the retrieval result by additional propagation processes. In order to speed up the convergence of the query concept, we adopt two active learning schemes to select images during relevance feedback. Furthermore, by means of keyword model update, the system can be self-improved constantly. The updating procedure can be performed online during relevance feedback without extra offline training. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images validate the effectiveness of the proposed method.</p> http://dx.doi.org/10.1155/ASP/2006/79412 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Li Mingjing Ma Wei-Ying Zhang Hong-Jiang Tong Hanghang He Jingrui Zhang Changshui |
spellingShingle |
Li Mingjing Ma Wei-Ying Zhang Hong-Jiang Tong Hanghang He Jingrui Zhang Changshui Manifold-Ranking-Based Keyword Propagation for Image Retrieval EURASIP Journal on Advances in Signal Processing |
author_facet |
Li Mingjing Ma Wei-Ying Zhang Hong-Jiang Tong Hanghang He Jingrui Zhang Changshui |
author_sort |
Li Mingjing |
title |
Manifold-Ranking-Based Keyword Propagation for Image Retrieval |
title_short |
Manifold-Ranking-Based Keyword Propagation for Image Retrieval |
title_full |
Manifold-Ranking-Based Keyword Propagation for Image Retrieval |
title_fullStr |
Manifold-Ranking-Based Keyword Propagation for Image Retrieval |
title_full_unstemmed |
Manifold-Ranking-Based Keyword Propagation for Image Retrieval |
title_sort |
manifold-ranking-based keyword propagation for image retrieval |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2006-01-01 |
description |
<p/> <p>A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straightforward manner by exploring the relationship among all images in the feature space in the learning stage. In relevance feedback, the feedback information can be naturally incorporated to refine the retrieval result by additional propagation processes. In order to speed up the convergence of the query concept, we adopt two active learning schemes to select images during relevance feedback. Furthermore, by means of keyword model update, the system can be self-improved constantly. The updating procedure can be performed online during relevance feedback without extra offline training. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images validate the effectiveness of the proposed method.</p> |
url |
http://dx.doi.org/10.1155/ASP/2006/79412 |
work_keys_str_mv |
AT limingjing manifoldrankingbasedkeywordpropagationforimageretrieval AT maweiying manifoldrankingbasedkeywordpropagationforimageretrieval AT zhanghongjiang manifoldrankingbasedkeywordpropagationforimageretrieval AT tonghanghang manifoldrankingbasedkeywordpropagationforimageretrieval AT hejingrui manifoldrankingbasedkeywordpropagationforimageretrieval AT zhangchangshui manifoldrankingbasedkeywordpropagationforimageretrieval |
_version_ |
1725371869752721408 |