Local and global multiscale image classification

Jean-Marc Boucher; Goze Benie; Regis Fau; Stephane Plehiers

doi:10.1117/12.188799

11 October 1994 Local and global multiscale image classification

Jean-Marc Boucher, Goze Benie, Regis Fau, Stephane Plehiers

Proceedings Volume 2303, Wavelet Applications in Signal and Image Processing II; (1994) https://doi.org/10.1117/12.188799
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

Different unsupervised Bayesian classification algorithms can be associated to a multiscale image analysis procedure leading to improvements, both in computation time and classification performances. Two kinds of algorithms are used for the classification itself: (1) local methods on a pixel-by-pixel basis and (2) global methods, which require a Markov random field model for the whole class image. Unsupervised Bayesian classification requires two steps, one for the parameter estimation of each local or global mode and one for the Bayesian classification itself. A Gaussian density with parameters depending on the class is assumed for the pixels. In a multiscale analysis scheme, the image is decomposed by successive filtering and downsampling, which allows to separate homogeneous areas and edges according to a pyramidal structure. One scale pyramid containing smaller and smaller smoothed images and one wavelet pyramid with the complementary information concerning details are built. Unsupervised Bayesian classification is done at each level of the scale pyramid, from top to bottom, by taking into account pixels which are assumed well classified at the previous level. The wavelet pyramid can be used to help the classification by defining if a classified pixel belongs to an homogeneous area or not. The homogeneity criterion consists in a variance comparison at each stage and a thresholding. A comparison has been made on very noisy synthetic images, which permits to measure the improvements and drawbacks brought by the multiscale analysis in local and global classification.

Citation Download Citation

Jean-Marc Boucher, Goze Benie, Regis Fau, and Stephane Plehiers "Local and global multiscale image classification", Proc. SPIE 2303, Wavelet Applications in Signal and Image Processing II, (11 October 1994); https://doi.org/10.1117/12.188799

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