An adaptive and progressive approach for efficient gradient-based multiresolution color image segmentation

  • Authors:

  • Sreenath Rao Vantaram;Eli Saber;Sohail Dianat;Mark Shaw;Ranjit Bhaskar

  • Affiliations:

  • Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY;Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY and Department of Electrical Engineering, Rochester Institute of Technology, Rochester, NY;Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY and Department of Electrical Engineering, Rochester Institute of Technology, Rochester, NY;Color and Imaging Division, Hewlett Packard Company, Boise, ID;Color and Imaging Division, Hewlett Packard Company, Boise, ID

  • Venue:
  • ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
  • Year:
  • 2009

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Abstract

We propose an image segmentation methodology which exploits gradient information in a multiresolution framework. The proposed algorithm commences with a wavelet decomposition procedure to obtain a pyramidal representation of the input image, accompanied by an adaptive threshold generation scheme required for segregating regions of varying gradient densities. At low (coarse) resolution levels, progressive region growth, texture characterization, and region merging modules are integrated together to provide interim segmentations. These interim results are transferred from one resolution level to another as a-priori information, until the final result at the highest (original) resolution is achieved. Performance evaluation on several hundred images demonstrates that our algorithm computationally outperforms various published techniques, with superior segmentation quality.