Region-based image retrieval using radial basis function network

Kui Wu; Kim Hui Yap; Lap Pui Chau

doi:10.1109/ICME.2006.262896

Region-based image retrieval using radial basis function network

Kui Wu, Kim Hui Yap, Lap Pui Chau

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

This paper presents a new framework that integrates relevance feedback into region-based image retrieval (RBIR) systems based on radial basis function network (RBFN). A modified unsupervised subtractive clustering algorithm is proposed for RBFN center selection according to the characteristics of region-based image representation. A new kernel function of RBFN is introduced for image similarity comparison under region-based representation. The underlying network parameters (weight and width) are then optimized using a supervised gradient-descent training strategy. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed hybrid learning approach.

Original language	English
Title of host publication	2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
Pages	1777-1780
Number of pages	4
DOIs	https://doi.org/10.1109/ICME.2006.262896
Publication status	Published - Jul 2006
Externally published	Yes
Event	2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Toronto, ON, Canada Duration: 9 Jul 2006 → 12 Jul 2006

Publication series

Name	2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
Volume	2006

Conference

Conference	2006 IEEE International Conference on Multimedia and Expo, ICME 2006
Country/Territory	Canada
City	Toronto, ON
Period	9/07/06 → 12/07/06

ASJC Scopus subject areas

Media Technology
Electrical and Electronic Engineering

More information

10.1109/ICME.2006.262896

Cite this

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title = "Region-based image retrieval using radial basis function network",

abstract = "This paper presents a new framework that integrates relevance feedback into region-based image retrieval (RBIR) systems based on radial basis function network (RBFN). A modified unsupervised subtractive clustering algorithm is proposed for RBFN center selection according to the characteristics of region-based image representation. A new kernel function of RBFN is introduced for image similarity comparison under region-based representation. The underlying network parameters (weight and width) are then optimized using a supervised gradient-descent training strategy. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed hybrid learning approach.",

author = "Kui Wu and Yap, \{Kim Hui\} and Chau, \{Lap Pui\}",

year = "2006",

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series = "2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings",

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note = "2006 IEEE International Conference on Multimedia and Expo, ICME 2006 ; Conference date: 09-07-2006 Through 12-07-2006",

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Wu, K, Yap, KH & Chau, LP 2006, Region-based image retrieval using radial basis function network. in 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings., 4036965, 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings, vol. 2006, pp. 1777-1780, 2006 IEEE International Conference on Multimedia and Expo, ICME 2006, Toronto, ON, Canada, 9/07/06. https://doi.org/10.1109/ICME.2006.262896

Region-based image retrieval using radial basis function network. / Wu, Kui; Yap, Kim Hui; Chau, Lap Pui.
2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings. 2006. p. 1777-1780 4036965 (2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings; Vol. 2006).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This paper presents a new framework that integrates relevance feedback into region-based image retrieval (RBIR) systems based on radial basis function network (RBFN). A modified unsupervised subtractive clustering algorithm is proposed for RBFN center selection according to the characteristics of region-based image representation. A new kernel function of RBFN is introduced for image similarity comparison under region-based representation. The underlying network parameters (weight and width) are then optimized using a supervised gradient-descent training strategy. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed hybrid learning approach.

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Region-based image retrieval using radial basis function network

Abstract

Publication series

Conference

ASJC Scopus subject areas

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