Paper Info
Title
Joint linear-circular stochastic models for texture classification
Abstract
In this paper, we investigate both linear and circular stochastic models in the context of texture discrimination. These models aim at representing the magnitudes and orientations obtained by a complex wavelet decomposition, such as the steerable pyramid.The novelty consists in considering specific parametric models for circular data such as von Mises and ψ- distributions to describe the distributions of orientations. Particular attention is paid to the choice of a metric and to its adequation to the models. Indexing experiments are conducted to quantitatively evaluate the performances of the proposed models and of the chosen matrices, i.e. the L1 and Kullback-Leibler distances.
Year
DOI
Venue
2009
10.1109/ICASSP.2009.4959773
ICASSP
Keywords
Field
DocType
complex wavelet decomposition,steerable pyramid,texture classification,kullback-leibler distance,indexing experiment,chosen matrix,circular stochastic model,specific parametric model,circular data,joint linear-circular stochastic model,particular attention,stochastic processes,stochastic model,image texture,computational modeling,histograms,parametric statistics,statistical distribution,frequency,parametric model,matrix decomposition,orientation,image processing,indexing,statistical distributions,probability density function,data models,filter bank,image classification,texture,context modeling,gamma distribution,kullback leibler distance,wavelet transforms,indexation
Parametric model,Pattern recognition,Matrix (mathematics),Image texture,Stochastic process,Probability distribution,Parametric statistics,Artificial intelligence,Stochastic modelling,Kullback–Leibler divergence,Mathematics
Conference
ISSN
Citations 
PageRank 
1520-6149
4
0.53
References 
Authors
5
5
Name
Order
Citations
PageRank
Marie-Cecile Peron140.53
Jean-Pierre Da Costa2192.48
Youssef Stitou3513.63
Christian Germain411318.95
Y. Berthoumieu538951.66