Multi-Class Constrained Normalized Cut With Hard, Soft, Unary and Pairwise Priors and its Applications to Object Segmentation

Normalized cut is a powerful method for image segmentation as well as data clustering. However, it does not perform well in challenging segmentation problems, such as segmenting objects in a complex background. Researchers have attempted to incorporate priors or constraints to handle such cases. Ava...

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Veröffentlicht in:IEEE transactions on image processing Jg. 22; H. 11; S. 4328 - 4340
Hauptverfasser: Hu, Han, Feng, Jianjiang, Yu, Chuan, Zhou, Jie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.11.2013
Institute of Electrical and Electronics Engineers
Schlagworte:
Accuracy
Algorithms
Applied sciences
Clustering algorithms
Constrained spectral clustering
Correlation
Exact sciences and technology
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image segmentation
Information, signal and communications theory
Matrix decomposition
Motion segmentation
Object detection
Object segmentation
Optimization
pairwise priors
Pattern recognition
Pattern Recognition, Automated - methods
Photography - methods
Reproducibility of Results
Sensitivity and Specificity
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Subtraction Technique
Synthetic data
Telecommunications and information theory
unary priors
Performance evaluation
Automatic classification
State of the art
Image processing
Background
pairwise priors
Pattern recognition
Shape detection
Algorithm
Signal classification
Image segmentation
Accuracy
object segmentation
unary priors
Constrained spectral clustering
Edge detection
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Normalized cut is a powerful method for image segmentation as well as data clustering. However, it does not perform well in challenging segmentation problems, such as segmenting objects in a complex background. Researchers have attempted to incorporate priors or constraints to handle such cases. Available priors in image segmentation problems may be hard or soft, unary or pairwise, but only hard must-link constraints and two-class settings are well studied. The main difficulties may lie in the following aspects: 1) the nontransitive nature of cannot-link constraints makes it hard to use such constraints in multi-class settings and 2) in multi-class or pairwise settings, the output labels have inconsistent representations with given priors, making soft priors difficult to use. In this paper, we propose novel algorithms, which can handle both hard and soft, both unary and pairwise priors in multi-class settings and provide closed form and efficient solutions. We also apply the proposed algorithms to the problem of object segmentation, producing good results by further introducing a spatial regularity term. Experiments show that the proposed algorithms outperform the state-of-the-art algorithms significantly in clustering accuracy. Other merits of the proposed algorithms are also demonstrated.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2013.2271865