B-spline over-complete wavelet based fractal signature analysis for texture image retrieval

Qing Wang; David D. Feng; Zheru Chi

doi:10.1109/ISIMP.2004.1434100

B-spline over-complete wavelet based fractal signature analysis for texture image retrieval

Qing Wang, David D. Feng, Zheru Chi

The Hong Kong Polytechnic University

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

2 Citations (Scopus)

Abstract

In the paper, we proposed a novel over-complete B-Spline wavelet based statistical features and fractal signatures for texture image analysis and retrieval. The discrete wavelet frame took the first order derivative of smoothing function into account, which is equivalent to Canny edge detection, with the specific case using Gaussian function as smoothing function. Meanwhile, the feature set based on the fractal surface area function in a Besov space is very accurate and robust for gray scale texture classification. Experimental results have shown that the proposed method is reasonable to describe the characteristics of the texture in temporal-frequent and fractal domain and it can reach the highest retrieval rate comparing with Gabor Filter based feature descriptor and B-Spline over-complete wavelet transformation based feature representation only.

Original language	English
Title of host publication	2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Pages	462-466
Number of pages	5
DOIs	https://doi.org/10.1109/ISIMP.2004.1434100
Publication status	Published - 1 Dec 2004
Event	2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004 - Hong Kong, China, Hong Kong Duration: 20 Oct 2004 → 22 Oct 2004

Conference

Conference	2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Country/Territory	Hong Kong
City	Hong Kong, China
Period	20/10/04 → 22/10/04

Keywords

B-Spline over-complete wavelet
Content based image retrieval
Gabor filter
Texture analysis
Wavelet-based fractal signature

ASJC Scopus subject areas

General Engineering

More information

10.1109/ISIMP.2004.1434100

Cite this

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title = "B-spline over-complete wavelet based fractal signature analysis for texture image retrieval",

abstract = "In the paper, we proposed a novel over-complete B-Spline wavelet based statistical features and fractal signatures for texture image analysis and retrieval. The discrete wavelet frame took the first order derivative of smoothing function into account, which is equivalent to Canny edge detection, with the specific case using Gaussian function as smoothing function. Meanwhile, the feature set based on the fractal surface area function in a Besov space is very accurate and robust for gray scale texture classification. Experimental results have shown that the proposed method is reasonable to describe the characteristics of the texture in temporal-frequent and fractal domain and it can reach the highest retrieval rate comparing with Gabor Filter based feature descriptor and B-Spline over-complete wavelet transformation based feature representation only.",

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doi = "10.1109/ISIMP.2004.1434100",

language = "English",

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Wang, Q, Feng, DD & Chi, Z 2004, B-spline over-complete wavelet based fractal signature analysis for texture image retrieval. in 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004. pp. 462-466, 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004, Hong Kong, China, Hong Kong, 20/10/04. https://doi.org/10.1109/ISIMP.2004.1434100

B-spline over-complete wavelet based fractal signature analysis for texture image retrieval. / Wang, Qing; Feng, David D.; Chi, Zheru.
2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004. 2004. p. 462-466.

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

TY - GEN

T1 - B-spline over-complete wavelet based fractal signature analysis for texture image retrieval

AU - Wang, Qing

AU - Feng, David D.

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N2 - In the paper, we proposed a novel over-complete B-Spline wavelet based statistical features and fractal signatures for texture image analysis and retrieval. The discrete wavelet frame took the first order derivative of smoothing function into account, which is equivalent to Canny edge detection, with the specific case using Gaussian function as smoothing function. Meanwhile, the feature set based on the fractal surface area function in a Besov space is very accurate and robust for gray scale texture classification. Experimental results have shown that the proposed method is reasonable to describe the characteristics of the texture in temporal-frequent and fractal domain and it can reach the highest retrieval rate comparing with Gabor Filter based feature descriptor and B-Spline over-complete wavelet transformation based feature representation only.

AB - In the paper, we proposed a novel over-complete B-Spline wavelet based statistical features and fractal signatures for texture image analysis and retrieval. The discrete wavelet frame took the first order derivative of smoothing function into account, which is equivalent to Canny edge detection, with the specific case using Gaussian function as smoothing function. Meanwhile, the feature set based on the fractal surface area function in a Besov space is very accurate and robust for gray scale texture classification. Experimental results have shown that the proposed method is reasonable to describe the characteristics of the texture in temporal-frequent and fractal domain and it can reach the highest retrieval rate comparing with Gabor Filter based feature descriptor and B-Spline over-complete wavelet transformation based feature representation only.

KW - B-Spline over-complete wavelet

KW - Content based image retrieval

KW - Gabor filter

KW - Texture analysis

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B-spline over-complete wavelet based fractal signature analysis for texture image retrieval

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Keywords

ASJC Scopus subject areas

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