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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Randomized neural network based signature for dynamic texture classification

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Author(s):
de Mesquita Sa Junior, Jarbas Joaci [1] ; Ribas, Lucas Correia [2] ; Bruno, Odemir Martinez [3]
Total Authors: 3
Affiliation:
[1] Univ Fed Ceara, Programa Posgrad Engn Eletr & Comp, Curso Engn Computacao, Campus Sobral, Rua Coronet Estanislau Frota 563, BR-62010560 Sobral, Ceara - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Stio Carlene 400, BR-13566590 Sao Carlos, SP - Brazil
[3] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: EXPERT SYSTEMS WITH APPLICATIONS; v. 135, p. 194-200, NOV 30 2019.
Web of Science Citations: 1
Abstract

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)

FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
Grantee:Lucas Correia Ribas
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 16/18809-9 - Deep learning and complex networks applied to computer vision
Grantee:Odemir Martinez Bruno
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
Grantee:Odemir Martinez Bruno
Support type: Regular Research Grants