Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Stio Carlene 400, BR-13566590 Sao Carlos, SP - Brazil
 Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil
Número total de Afiliações: 3
Tipo de documento:
EXPERT SYSTEMS WITH APPLICATIONS;
NOV 30 2019.
Citações Web of Science:
Dynamic texture analysis has been the focus of intensive research in recent years. Thus, this paper presents an innovative and highly discriminative dynamic texture analysis method, whose signature is composed of the weights of the output layer of a randomized neural network after a training procedure. This training is performed by using the pixels of slices of each orthogonal plane of the video (XY, YT, and XT) as input feature vectors and corresponding output labels. The obtained video signature provided an accuracy of 97.05%, 98.54%, 97.74% and 96.51% on the UCLA-50 classes, UCLA-9 classes, UCLA-8 classes and Dyntex++, respectively. These results, when compared to other dynamic texture analysis methods, demonstrate that our descriptors are very effective and that our proposed approach can contribute significantly to the field of dynamic texture analysis. (C) 2019 Elsevier Ltd. All rights reserved. (AU)