Constrained spectral clustering for image segmentation

Constrained spectral clustering with affinity propagation in its original form is not practical for large scale problems like image segmentation. In this paper we employ novelty selection sub-sampling strategy, besides using efficient numerical eigen-decomposition methods to make this algorithm work...

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Bibliographic Details
Main Authors: Sourati, J., Brooks, D. H., Dy, J. G., Erdogmus, D.
Format: Conference Proceeding
Language:English
Subjects:
active learning
Clustering algorithms
Constrained spectral clustering
Equations
Image segmentation
Machine learning
Matrices
Signal processing algorithms
Vectors
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Summary:Constrained spectral clustering with affinity propagation in its original form is not practical for large scale problems like image segmentation. In this paper we employ novelty selection sub-sampling strategy, besides using efficient numerical eigen-decomposition methods to make this algorithm work efficiently for images. In addition, entropy-based active learning is also employed to select the queries posed to the user more wisely in an interactive image segmentation framework. We evaluate the algorithm on general and medical images to show that the segmentation results will improve using constrained clustering even if one works with a subset of pixels. Furthermore, this happens more efficiently when pixels to be labeled are selected actively.
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2012.6349765