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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Image segmentation by probabilistic bottom-up aggregation and cue integration.

Sharon Alpert1, Meirav Galun, Achi Brandt

  • 1Faculty of Mathematics and Computer Science, Weizmann Institute of Science, PO Box 26, Rehovot 76100, Israel. sharon.alpert@weizmann.ac.il

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 22, 2011
PubMed
Summary
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This study introduces a novel bottom-up image segmentation method using probabilistic region merging. The approach efficiently creates hierarchical image segments with minimal user input and a new evaluation scheme.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image segmentation is crucial for image analysis.
  • Existing methods often require significant parameter tuning or semantic understanding.
  • Hierarchical segmentation provides multi-scale representations.

Purpose of the Study:

  • To develop an automated, bottom-up image segmentation algorithm.
  • To create a hierarchical segmentation of images.
  • To introduce a novel, semantics-free evaluation metric for segmentation algorithms.

Main Methods:

  • A bottom-up aggregation approach starting from individual pixels.
  • Probabilistic region merging considering intensity, texture, and geometric priors.
  • Integration into a graph coarsening scheme for hierarchical segmentation.

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  • A novel evaluation scheme independent of human semantic interpretation.

    • Main Results:

    • The algorithm produces a complete hierarchical segmentation.
    • Computational complexity is linear with respect to the number of image pixels.
    • The method requires minimal user-tuned parameters.
    • Performance was validated using the novel evaluation scheme and compared to existing algorithms.

    Conclusions:

    • The proposed probabilistic bottom-up approach offers an efficient and automated method for hierarchical image segmentation.
    • The novel evaluation scheme provides an objective measure for segmentation quality.
    • This method has potential for various image analysis applications requiring robust segmentation.