Multi-model similarity propagation and its application for Web image retrieval

Xin-Jing Wang; Wei-Ying Ma; Gui-Rong Xue; Xing Li

doi:10.1145/1027527.1027746

Multi-model similarity propagation and its application for Web image retrieval

Xin-Jing Wang
, Wei-Ying Ma
, Gui-Rong Xue
, Xing Li

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

89 Citations (Scopus)

Abstract

In this paper, we propose an iterative similarity propagation approach to explore the inter-relationships between Web images and their textual annotations for image retrieval. By considering Web images as one type of objects, their surrounding texts as another type, and constructing the links structure between them via webpage analysis, we can iteratively reinforce the similarities between images. The basic idea is that if two objects of the same type are both related to one object of another type, these two objects are similar; likewise, if two objects of the same type are related to two different, but similar objects of another type, then to some extent, these two objects are also similar. The goal of our method is to fully exploit the mutual reinforcement between images and their textual annotations. Our experiments based on 10,628 images crawled from the Web show that our proposed approach can significantly improve Web image retrieval performance.
Copyright 2004 ACM

Original language	English
Title of host publication	ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
Publisher	Association for Computing Machinery
Pages	944-951
ISBN (Print)	1581138938, 9781581138931
DOIs	https://doi.org/10.1145/1027527.1027746
Publication status	Published - 2004
Externally published	Yes
Event	ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia - New York, NY, United States Duration: 10 Oct 2004 → 16 Oct 2004

Publication series

Name	ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia

Conference

Conference	ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
Place	United States
City	New York, NY
Period	10/10/04 → 16/10/04

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Iterative Similarity Propagation
Mixture Model
Multimedia Retrieval
Mutual Reinforcement

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10.1145/1027527.1027746

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Wang, X.-J., Ma, W.-Y., Xue, G.-R., & Li, X. (2004). Multi-model similarity propagation and its application for Web image retrieval. In ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia (pp. 944-951). (ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia). Association for Computing Machinery. https://doi.org/10.1145/1027527.1027746

Wang, Xin-Jing ; Ma, Wei-Ying ; Xue, Gui-Rong et al. / Multi-model similarity propagation and its application for Web image retrieval. ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia. Association for Computing Machinery, 2004. pp. 944-951 (ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia).

@inproceedings{36341abf0ae04eb69141af4a70149984,

title = "Multi-model similarity propagation and its application for Web image retrieval",

abstract = "In this paper, we propose an iterative similarity propagation approach to explore the inter-relationships between Web images and their textual annotations for image retrieval. By considering Web images as one type of objects, their surrounding texts as another type, and constructing the links structure between them via webpage analysis, we can iteratively reinforce the similarities between images. The basic idea is that if two objects of the same type are both related to one object of another type, these two objects are similar; likewise, if two objects of the same type are related to two different, but similar objects of another type, then to some extent, these two objects are also similar. The goal of our method is to fully exploit the mutual reinforcement between images and their textual annotations. Our experiments based on 10,628 images crawled from the Web show that our proposed approach can significantly improve Web image retrieval performance. Copyright 2004 ACM",

keywords = "Iterative Similarity Propagation, Mixture Model, Multimedia Retrieval, Mutual Reinforcement",

author = "Xin-Jing Wang and Wei-Ying Ma and Gui-Rong Xue and Xing Li",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia ; Conference date: 10-10-2004 Through 16-10-2004",

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doi = "10.1145/1027527.1027746",

language = "English",

isbn = "1581138938",

series = "ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia",

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Wang, X-J, Ma, W-Y, Xue, G-R & Li, X 2004, Multi-model similarity propagation and its application for Web image retrieval. in ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia. ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia, Association for Computing Machinery, pp. 944-951, ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia, New York, NY, United States, 10/10/04. https://doi.org/10.1145/1027527.1027746

Multi-model similarity propagation and its application for Web image retrieval. / Wang, Xin-Jing; Ma, Wei-Ying; Xue, Gui-Rong et al.
ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia. Association for Computing Machinery, 2004. p. 944-951 (ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Multi-model similarity propagation and its application for Web image retrieval

AU - Wang, Xin-Jing

AU - Ma, Wei-Ying

AU - Xue, Gui-Rong

AU - Li, Xing

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2004

Y1 - 2004

N2 - In this paper, we propose an iterative similarity propagation approach to explore the inter-relationships between Web images and their textual annotations for image retrieval. By considering Web images as one type of objects, their surrounding texts as another type, and constructing the links structure between them via webpage analysis, we can iteratively reinforce the similarities between images. The basic idea is that if two objects of the same type are both related to one object of another type, these two objects are similar; likewise, if two objects of the same type are related to two different, but similar objects of another type, then to some extent, these two objects are also similar. The goal of our method is to fully exploit the mutual reinforcement between images and their textual annotations. Our experiments based on 10,628 images crawled from the Web show that our proposed approach can significantly improve Web image retrieval performance. Copyright 2004 ACM

AB - In this paper, we propose an iterative similarity propagation approach to explore the inter-relationships between Web images and their textual annotations for image retrieval. By considering Web images as one type of objects, their surrounding texts as another type, and constructing the links structure between them via webpage analysis, we can iteratively reinforce the similarities between images. The basic idea is that if two objects of the same type are both related to one object of another type, these two objects are similar; likewise, if two objects of the same type are related to two different, but similar objects of another type, then to some extent, these two objects are also similar. The goal of our method is to fully exploit the mutual reinforcement between images and their textual annotations. Our experiments based on 10,628 images crawled from the Web show that our proposed approach can significantly improve Web image retrieval performance. Copyright 2004 ACM

KW - Iterative Similarity Propagation

KW - Mixture Model

KW - Multimedia Retrieval

KW - Mutual Reinforcement

UR - https://www.scopus.com/pages/publications/13444282390

UR - https://www.scopus.com/pages/publications/13444282390#tab=impact

U2 - 10.1145/1027527.1027746

DO - 10.1145/1027527.1027746

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 1581138938

SN - 9781581138931

T3 - ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia

SP - 944

EP - 951

BT - ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia

PB - Association for Computing Machinery

T2 - ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia

Y2 - 10 October 2004 through 16 October 2004

ER -

Wang XJ, Ma WY, Xue GR, Li X. Multi-model similarity propagation and its application for Web image retrieval. In ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia. Association for Computing Machinery. 2004. p. 944-951. (ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia). doi: 10.1145/1027527.1027746

Multi-model similarity propagation and its application for Web image retrieval

Abstract

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Bibliographical note

Research Keywords

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