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 aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and prop...

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Vydáno v:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition s. 731 - 737
Hlavní autoři: Jianbo Shi, Malik, J.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 1997
Témata:
Brightness
Clouds
Clustering algorithms
Computer science
Data mining
Eigenvalues and eigenfunctions
Humans
Image segmentation
Layout
Pixel
ISBN:9780818678226, 0818678224
ISSN:1063-6919, 1063-6919
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Shrnutí: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.
ISBN:9780818678226
0818678224
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.1997.609407