Dynamic texture classification using Gumbel mixtures in the complex wavelet domain

作     者：Qiao, Yulong Liu, Qiufei Liu, Wenhui 

作者机构：Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2019年第13卷第1期

页      面：9-14页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China 

主　　题：expectation-maximisation algorithm trees (mathematics) image sequences image texture wavelet transforms image classification statistical distributions approximation theory DT features MoGD model DT classification dual-tree complex wavelet DT-CWT complex wavelet coefficient magnitudes nonoverlapping blocks feature vector dynamic texture classification Gumbel mixtures complex wavelet domain image sequence analysis probability distribution model parameter estimation method expectation-maximisation algorithm signal distribution property finite mixtures-of-Gumbel distributions median values benchmark DT data sets UCLA Kullback-Leibler divergence DynTex plus 

摘      要：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.