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A framework based on the Affine Invariant Regions for improving unsupervised image segmentation

Mostajabi, M ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1109/ISSPA.2012.6310541
  3. Publisher: 2012
  4. Abstract:
  5. Processing time and segmentation quality are two main factors in evaluation of image segmentation methods. Oversegmentation is one of the most challenging problems that significantly degrade the segmentation quality. This paper presents a framework for decreasing the oversegmentation rate and improving the processing time. Significant variations in both color and texture spaces are the main reasons that lead to oversegmentation. We exploit Affine Invariant Region Detectors to mark regions with high variations in both color and texture spaces. The results are then utilized to reduce the oversegmentation rate of image segmentation algorithms. The performance of the proposed framework is evaluated in decreasing the oversegmentation rate of the well-known Mean Shift method. In conjunction with the proposed framework, we have applied some optimizations on the Mean Shift method to reduce the processing time. In comparison with the original Mean Shift, our experimental results show a twofold speedup and improved segmentation quality
  6. Keywords:
  7. Affine Invariant Regions ; Affine invariant ; Image segmentation algorithm ; Mark regions ; Mean shift ; Over segmentation ; Processing time ; Segmentation methods ; Segmentation quality ; Texture space ; Unsupervised image segmentation ; Unsupervised segmentation ; Information science ; Quality control ; Signal processing ; Textures ; Image segmentation
  8. Source: 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 ; 2012 , Pages 17-22 ; 9781467303828 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6310541