Open AccessDynamic texture classification using Gumbel mixtures in the complex wavelet domain
Author(s) -
Qiao Yulong,
Liu Qiufei,
Liu Wenhui
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5363
Subject(s) - complex wavelet transform , pattern recognition (psychology) , wavelet , mathematics , gumbel distribution , divergence (linguistics) , artificial intelligence , wavelet transform , feature vector , algorithm , sequence (biology) , feature (linguistics) , probability distribution , similarity (geometry) , computer science , discrete wavelet transform , image (mathematics) , statistics , extreme value theory , linguistics , philosophy , biology , genetics
Dynamic texture (DT) classification has attracted extensive attention in the field of image sequence analysis. The probability distribution model, which has been used to analysis DT, can describe well the distribution property of signals. Here, the authors introduce the finite mixtures of Gumbel distributions (MoGD) and the corresponding parameter estimation method based on expectation–maximisation algorithm. Then, the authors propose the DT features based on MoGD model for DT classification. Specifically, after decomposing DTs with the dual‐tree complex wavelet transform (DT‐CWT), the median values of complex wavelet coefficient magnitudes of non‐overlapping blocks in detail subbands are modelled with MoGDs. The model parameters are accumulated into a feature vector to describe DT. During the classification, a variational approximation version of the Kullback–Leibler divergence is used to measure the similarity between different DTs. The experimental evaluations on two popular benchmark DT data sets (UCLA and DynTex++) demonstrate the effectiveness of the proposed approach.