Multiresolution analysis of connectivity

  • Authors:

  • Atul Sajjanhar;Guojun Lu;Dengsheng Zhang;Tian Qi

  • Affiliations:

  • School of Information Technology, Deakin University, Burwood, Australia;Gippsland School of Computing & Information Technology, Monash University, Churchill, Australia;Gippsland School of Computing & Information Technology, Monash University, Churchill, Australia;Media Division, Institute for Infocomm Research, Singapore

  • Venue:
  • IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
  • Year:
  • 2005

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Abstract

Multiresolution histograms have been used for indexing and retrieval of images. Multiresolution histograms used traditionally are 2d-histograms which encode pixel intensities. Earlier we proposed a method for decomposing images by connectivity. In this paper, we propose to encode centroidal distances of an image in multiresolution histograms; the image is decomposed a priori, by connectivity. Multiresolution histograms thus obtained are 3d-histograms which encode connectivity and centroidal distances. The statistical technique of Principal Component Analysis is applied to multiresolution 3d-histograms and the resulting data is used to index images. Distance between two images is computed as the L2-difference of their principal components. Experiments are performed on Item S8 within the MPEG-7 image dataset. We also analyse the effect of pixel intensity thresholding on multiresolution images.