An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction

Lulu He; Thrasyvoulos N Pappas

doi:10.1109/ICIP.2010.5651905

An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction

Lulu He^*, Thrasyvoulos N Pappas

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

We present a novel, computationally efficient approach for natural image segmentation that uses the adaptive clustering algorithm (ACA) to obtain an initial segmentation and chrominance-based region merging to consolidate regions of perceptually uniform texture. The combination of ACA and chrominance-based merging preserves salient edges and smooths out noise and edges within textured regions. It can thus be used for image abstraction. Experimental results with natural images indicate the effectiveness of the proposed approach.

Original language	English (US)
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	241-244
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5651905
State	Published - Dec 1 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: Sep 26 2010 → Sep 29 2010

Other

Other	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	9/26/10 → 9/29/10

Keywords

Adaptive clustering algorithm
Bilateral filtering
Region merging

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2010.5651905

Cite this

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title = "An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction",

abstract = "We present a novel, computationally efficient approach for natural image segmentation that uses the adaptive clustering algorithm (ACA) to obtain an initial segmentation and chrominance-based region merging to consolidate regions of perceptually uniform texture. The combination of ACA and chrominance-based merging preserves salient edges and smooths out noise and edges within textured regions. It can thus be used for image abstraction. Experimental results with natural images indicate the effectiveness of the proposed approach.",

keywords = "Adaptive clustering algorithm, Bilateral filtering, Region merging",

author = "Lulu He and Pappas, {Thrasyvoulos N}",

year = "2010",

month = dec,

day = "1",

doi = "10.1109/ICIP.2010.5651905",

language = "English (US)",

isbn = "9781424479948",

pages = "241--244",

booktitle = "2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings",

note = "2010 17th IEEE International Conference on Image Processing, ICIP 2010 ; Conference date: 26-09-2010 Through 29-09-2010",

}

He, L & Pappas, TN 2010, An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction. in 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings., 5651905, pp. 241-244, 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, Hong Kong, 9/26/10. https://doi.org/10.1109/ICIP.2010.5651905

TY - GEN

T1 - An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction

AU - He, Lulu

AU - Pappas, Thrasyvoulos N

PY - 2010/12/1

Y1 - 2010/12/1

N2 - We present a novel, computationally efficient approach for natural image segmentation that uses the adaptive clustering algorithm (ACA) to obtain an initial segmentation and chrominance-based region merging to consolidate regions of perceptually uniform texture. The combination of ACA and chrominance-based merging preserves salient edges and smooths out noise and edges within textured regions. It can thus be used for image abstraction. Experimental results with natural images indicate the effectiveness of the proposed approach.

AB - We present a novel, computationally efficient approach for natural image segmentation that uses the adaptive clustering algorithm (ACA) to obtain an initial segmentation and chrominance-based region merging to consolidate regions of perceptually uniform texture. The combination of ACA and chrominance-based merging preserves salient edges and smooths out noise and edges within textured regions. It can thus be used for image abstraction. Experimental results with natural images indicate the effectiveness of the proposed approach.

KW - Adaptive clustering algorithm

KW - Bilateral filtering

KW - Region merging

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U2 - 10.1109/ICIP.2010.5651905

DO - 10.1109/ICIP.2010.5651905

M3 - Conference contribution

AN - SCOPUS:78651085671

SN - 9781424479948

SP - 241

EP - 244

BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010

Y2 - 26 September 2010 through 29 September 2010

ER -

An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction

Abstract

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Keywords

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