Scalable multiresolution color image segmentation

  • Authors:

  • Fardin Akhlaghian Tab;Golshah Naghdy;Alfred Mertins

  • Affiliations:

  • School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia;School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia;Signal Processing Group, Institute of Physics, University of Oldenburg, Germany

  • Venue:
  • Signal Processing
  • Year:
  • 2006

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Abstract

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modeling. 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 suitable 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. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, particularly at lower resolutions where down-sampling distortions are more visible. 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.