Title
Co-clustering of image segments using convex optimization applied to EM neuronal reconstruction
Abstract
This paper addresses the problem of jointly clustering two segmentations of closely correlated images. We focus in particular on the application of reconstructing neuronal structures in over-segmented electron microscopy images. We formulate the problem of co-clustering as a quadratic semi-assignment problem and investigate convex relaxations using semidefinite and linear programming. We further introduce a linear programming method with manageable number of constraints and present an approach for learning the cost function. Our method increases computational efficiency by orders of magnitude while maintaining accuracy, automatically finds the optimal number of clusters, and empirically tends to produce binary assignment solutions. We illustrate our approach in simulations and in experiments with real EM data.
Year
DOI
Venue
2010
10.1109/CVPR.2010.5539901
CVPR
Keywords
Field
DocType
convex programming,electron microscopy,image reconstruction,image segmentation,medical image processing,pattern clustering,quadratic programming,EM neuronal reconstruction,convex optimization,cost function,image correlation,image segment co-clustering,linear programming,over-segmented electron microscopy image,quadratic semiassignment problem
Iterative reconstruction,Computer vision,Mathematical optimization,Computer science,Quadratic equation,Image segmentation,Linear programming,Artificial intelligence,Biclustering,Quadratic programming,Cluster analysis,Convex optimization
Conference
Volume
Issue
ISSN
2010
1
1063-6919
Citations 
PageRank 
References 
34
1.51
13
Authors
2
Name
Order
Citations
PageRank
Shiv Naga Prasad Vitaladevuni127218.18
Ronen Basri23467403.18