Abstract
We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging.
Keywords
Artificial intelligenceImage segmentationSegmentationPattern recognition (psychology)Segmentation-based object categorizationMarket segmentationScale-space segmentationComputer visionRange segmentationGraphComputer scienceImage (mathematics)Similarity (geometry)MathematicsImage textureTheoretical computer science
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Publication Info
- Year
- 2000
- Type
- article
- Volume
- 22
- Issue
- 8
- Pages
- 888-905
- Citations
- 15440
- Access
- Closed
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Cite This
Jianbo Shi,
Jitendra Malik
(2000).
Normalized cuts and image segmentation.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 22
(8)
, 888-905.
https://doi.org/10.1109/34.868688
Identifiers
- DOI
- 10.1109/34.868688