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An Unsupervised Stochastic Model for Detection and Identification of Objects in Textured Color Images Using Segmentation Technique

  • Mofakharul Islam
  • Paul A. Watters
Conference paper

Abstract

The process of meaningful image object identification is the critical first step in the extraction of image information for computer vision and image understanding. The disjoint regions correspond to visually distinct objects in a scene. In this particular work, we investigate and propose a novel stochastic model based approach to implement a robust unsupervised color image content understanding technique that segments a color textured image into its constituent parts automatically and meaningfully. The aim of this work is to produce precise segmentation of different objects in a color image using color information, texture information and neighborhood relationships among neighboring image pixels in terms of their features using Markov Random Field (MRF) Model to get the maximum accuracy in segmentation. The evaluation of the results is done through comparison of the segmentation quality and accuracy with another similar existing method which demonstrates that the proposed approach outperforms the existing method by achieving better segmentation accuracy with faithful segmentation results.

Keywords

Image Segmentation Texture Feature Color Space Gabor Filter Haar Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Internet Commerce and Security Laboratory, Graduate School of Information Technology and Mathematical SciencesUniversity of BallaratSydneyAustralia

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