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Unsupervised Whole-Object Segmentation: Integrating Automated Matting and Boundary Detection

This work explores a novel approach to unsupervised whole-object segmentation by combining automated matting techniques with boundary detection strategies. By leveraging multiple matting processes to generate segmentation hints and employing spectral clustering for image segmentation, this methodology aims to enhance the accuracy and efficiency of object segmentation. The paper outlines experimental results demonstrating promising outcomes, paving the way for future advancements in unsupervised object discovery and scene understanding.

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Unsupervised Whole-Object Segmentation: Integrating Automated Matting and Boundary Detection

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  1. Towards Unsupervised Whole-Object Segmentation:Combining Automated Matting with Boundary Detection Andrew N. Stein∗ Thomas S. Stepleton Martial Hebert The Robotics Institute, Carnegie Mellon University Reporter: Hsieh Chia-Hao Date: 2009/09/28

  2. Introduction • Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary Detection

  3. Outline • Segmentation “Hints” via Multiple Mattes • α-Matting • Multiple Mattes  Affinities • Detecting Boundary Fragments • Image Segmentation by Ncuts • Evaluating Object Segmentations • Experiments

  4. α-Matting Multiple Mattes • α-Matting • Multiple Mattes  Affinities Pixel OR super-pixels

  5. Detecting Boundary Fragments • Incorporating local, instantaneous motion estimates when short video clips are available • Over-segments a scene into a few hundred “super-pixels” using a watershed-based approach • Learned classifiers and inference on a graphical model  boundary probabilities (weight for W)

  6. Image Segmentation by Ncuts • Given pair-wise affinity matrix A • Use spectral clustering according to the Normalized Cut Criterion • Produce an image segmentation with K segments • K is variable

  7. Evaluating Object Segmentations • Consistency • Efficiency R = {A, B, C, . . . } ⊆S is a subset of segments from a given (over-)segmentation S

  8. Experiments

  9. Experiments bottom-up perspective

  10. Discussion • Promising results for use in subsequent work on unsupervised object discovery or scene understanding

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