Paper Info
Title
Texture image classification with Riemannian fisher vectors issued from a Laplacian model
Abstract
Many signal and image processing applications are based on the classification of covariance matrices. These latter are elements on a Riemannian manifold for which many generative models have been developed in the literature. Recently, the Riemannian Laplace distribution (RLD) has been proposed to model the within-class variability of images. In this context, the present paper proposes an application of RLDs to the definition of Riemannian Fisher vectors issued from this Laplacian model. The expression of these descriptors is derived for mixtures of RLDs and their relation with the Riemannian vectors of locally aggregated descriptors is shown. Some comparisons with the bag of Riemannian words model are also performed. All these aforementioned descriptors are applied to texture image classification to find the most discriminating one. Moreover, to determine the best model for fitting the data, the classification performances are compared to those given by the Riemannian Gaussian distribution.
Year
DOI
Venue
2016
10.1109/IVMSPW.2016.7528231
2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)
Keywords
Field
DocType
texture image classification,Laplacian model,image processing applications,covariance matrices,Riemannian manifold,generative models,Riemannian Laplace distribution,RLD,Riemannian Fisher vectors,Riemannian words model,Riemannian Gaussian distribution
Information geometry,Laplace distribution,Pattern recognition,Riemannian manifold,Image processing,Artificial intelligence,Contextual image classification,Statistical manifold,Exponential map (Riemannian geometry),Mathematics,Laplace operator
Conference
ISBN
Citations 
PageRank 
978-1-5090-1930-4
0
0.34
References 
Authors
12
4
Name
Order
Citations
PageRank
Ioana Ilea100.34
Lionel Bombrun215020.59
Christian Germain311318.95
Y. Berthoumieu438951.66