Paper Info
Title
Tensor processing for texture and colour segmentation
Abstract
In this paper, we propose an original approach for texture and colour segmentation based on the tensor processing of the nonlinear structure tensor. While the tensor structure is a well established tool for image segmentation, its advantages were only partly used because of the vector processing of that information. In this work, we use more appropriate definitions of tensor distance grounded in concepts from information theory and compare their performance on a large number of images. We clearly show that the traditional Frobenius norm-based tensor distance is not the most appropriate one. Symmetrized KL divergence and Riemannian distance intrinsic to the manifold of the symmetric positive definite matrices are tested and compared. Adding to that, the extended structure tensor and the compact structure tensor are two new concepts that we present to incorporate gray or colour information without losing the tensor properties. The performance and the superiority of the Riemannian based approach over some recent studies are demonstrated on a large number of gray-level and colour data sets as well as real images.
Year
DOI
Venue
2005
10.1007/11499145_113
SCIA
Keywords
Field
DocType
tensor property,tensor structure,colour data,large number,colour segmentation,nonlinear structure tensor,extended structure tensor,compact structure tensor,riemannian distance,tensor distance,tensor processing,image segmentation,information theory
Computer vision,Pattern recognition,Tensor (intrinsic definition),Tensor,Computer science,Image processing,Cartesian tensor,Image segmentation,Structure tensor,Artificial intelligence,Tensor contraction,Kullback–Leibler divergence
Conference
Volume
ISSN
ISBN
3540
0302-9743
3-540-26320-9
Citations 
PageRank 
References 
19
1.17
13
Authors
4
Name
Order
Citations
PageRank
Rodrigo de Luis-García115014.15
Rachid Deriche24903633.65
Mikael Rousson3100241.09
Carlos Alberola-López448252.95