Regression based bandwidth selection for segmentation using Parzen windows

Maneesh Singh, Narendra Ahuja

Research output: Contribution to conferencePaperpeer-review


We consider the problem of segmentation of images that can be modelled aspiecewise continuous signals having unknown, non-stationary statistics. We propose a solution to this problem which first uses a regression framework to estimate the image PDF, and then mean-shift to find the modes of this PDF. The segmentation follows from mode identified cation wherein pixel clusters or image segments are identified with unique modes of the multi-modal PDF. Each pixel is mapped to a mode using a convergent, iterative process. The effectiveness of the approach depends upon the accuracy of the (implicit)estimate of the underlying multi-modal density function and thus on the bandwidth parameters used for its estimate using Parzen windows. Automatic selection of bandwidth parameters is a desired feature of the algorithm. We show that the proposed regression-based model admits a realistic framework to automatically choose band width parameters which minimizes a global error criterion. We validate the theory presented with results on real images.

Original languageEnglish (US)
Number of pages8
StatePublished - 2003
EventProceedings: Ninth IEEE International Conference on Computer Vision - Nice, France
Duration: Oct 13 2003Oct 16 2003


OtherProceedings: Ninth IEEE International Conference on Computer Vision

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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