NDLI: Dynamic texture classification using Gumbel mixtures in the complex wavelet domain

Content Provider	IET Digital Library
Author	Qiao, Yulong Liu, Qiufei Liu, Wenhui
Abstract	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.
Starting Page	9
Ending Page	14
Page Count	6
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5363
Journal	IET Image Processing
Publisher Date	2018-09-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Approximation Theory Benchmark DT Data Sets Complex Wavelet Coefficient Magnitudes Complex Wavelet Domain Computer Vision And Image Processing Technique DT Classification DT Features DT-CWT Dual-tree Complex Wavelet Dynamic Texture Classification DynTex++ Expectation-maximisation Algorithm Feature Vector Finite Mixtures-of-Gumbel Distribution Gumbel Mixtures Image Classification Image Recognition Image Sequence Image Sequence Analysis Image Texture Integral Transforms in Numerical Analysis Interpolation And Function Approximation Kullback–Leibler Divergence Mathematics Median Value MoGD Model Nonoverlapping Blocks Numerical Analysis Parameter Estimation Method Probability Distribution Model Signal Distribution Property Statistical Distribution Statistics Trees UCLA Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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