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 have applied this approach to segmenting static images and found results very encouraging.
Keywords
Image segmentationArtificial intelligenceSegmentationPattern recognition (psychology)Segmentation-based object categorizationMarket segmentationImage (mathematics)Scale-space segmentationRange segmentationGraphSimilarity (geometry)Computer scienceComputer visionEigenvalues and eigenvectorsMathematicsTheoretical computer science
Affiliated Institutions
Related Publications
Normalized cuts and image segmentation
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...
Learning Segmentation by Random Walks
We present a new view of image segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvector...
Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization
We present a stochastic clustering algorithm which uses pairwise similarity of elements and show how it can be used to address various problems in computer vision, including the...
Multiscale Combinatorial Grouping
We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For ...
Global optimization using embedded graphs
One of the challenges of computer vision is that the information we seek to extract from images is not even defined for most images. Because of this, we cannot hope to find a si...
Publication Info
- Year
- 2002
- Type
- article
- Pages
- 731-737
- Citations
- 855
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
855
OpenAlex
Cite This
Jianbo Shi,
Jitendra Malik
(2002).
Normalized cuts and image segmentation.
, 731-737.
https://doi.org/10.1109/cvpr.1997.609407
Identifiers
- DOI
- 10.1109/cvpr.1997.609407