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Proceedings Paper

Scalable multiresolution color image segmentation
Author(s): Farin Akhlaghian Tab; Golshah Naghdy; Alfred Mertins
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Paper Abstract

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modelling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it applicable for scalable object-based wavelet coding. To optimize segmentation at all resolutions of the wavelet pyramid, with scalability constraint, a multiresolution analysis is incorporated into the objective function of the MRF segmentation algorithm. Examining the corresponding pixels at different resolutions simultaneously enables the algorithm to directly segment the images in the YUV or similar color spaces where luminance is in full resolution and chrominance components are at half resolution. In addition to spatial scalability, the proposed algorithm outperforms the standard single and multiresolution segmentation algorithms, in both objective and subjective tests, yielding an effective segmentation that particularly supports scalable object-based wavelet coding.

Paper Details

Date Published: 24 June 2005
PDF: 12 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59604X (24 June 2005); doi: 10.1117/12.633217
Show Author Affiliations
Farin Akhlaghian Tab, Univ. of Wollongong (Australia)
Golshah Naghdy, Univ. of Wollongong (Australia)
Alfred Mertins, Univ. of Oldenburg (Germany)

Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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