Tensor based image segmentation

In recent years spectral clustering has become on e of the most popular clustering algorithms. It is a simple yet powerful method for finding structure in data using spectral properties of an associated pairwise similarity matrix. It is simple to implement, can be solved efficiently by standard line...

Full description

Saved in:
Bibliographic Details
Main Authors: Jankovic, M., Sugiyama, M., Reljin, B.
Format: Conference Proceeding
Language:English
Subjects:
Born rule
Clustering algorithms
Clustering methods
Image segmentation
Linear algebra
Mathematics
multiway arrays
Neural networks
Software algorithms
Software standards
spectral clustering
Tensile stress
Working environment noise
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years spectral clustering has become on e of the most popular clustering algorithms. It is a simple yet powerful method for finding structure in data using spectral properties of an associated pairwise similarity matrix. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. In this paper we propose a new way of image segmentation based on specifically created similarity matrix and based on it, very simple segmentation algorithm. The algorithm is theoretically motivated and demonstrated on nontrivial examples.
DOI:10.1109/NEUREL.2008.4685595