Learning graph representation with Randomized Neural Network for dynamic texture classification

Autor(es)

Ribas, Lucas C. ^{[1, 2, 3]} ; de Mesquita Sa Junior, Jarbas Joaci ^[4] ; Manzanera, Antoine ^[3] ; Bruno, Odemir M. ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[3] Inst Polytech Paris, ENSTA Paris, U2IS, 828 Blvd Marechaux, F-91120 Palaiseau - France

^[4] Univ Fed Ceara, Ctr Sobral, Programa Posgrad Engn Elect & Comp, Curso Engn Comp, Campus Sobral Rua Coronel Estanislau Frota 563, BR-62010560 Fortaleza, Ceara - Brazil

• Tipo de documento: Artigo Científico
• Fonte: APPLIED SOFT COMPUTING; v. 114, JAN 2022.
• Citações Web of Science: 0

Resumo

Dynamic textures (DTs) are pseudo periodic data on a space x time support, that can represent many natural phenomena captured from video footages. Their modeling and recognition are useful in many applications of computer vision. This paper presents an approach for DT analysis combining a graph-based description from the Complex Network framework, and a learned representation from the Randomized Neural Network (RNN) model. First, a directed space x time graph modeling with only one parameter (radius) is used to represent both the motion and the appearance of the DT. Then, instead of using classical graph measures as features, the DT descriptor is learned using a RNN, that is trained to predict the gray level of pixels from local topological measures of the graph. The weight vector of the output layer of the RNN forms the descriptor. Several structures are experimented for the RNNs, resulting in networks with final characteristics of a single hidden layer of 4, 24, or 29 neurons, and input layers of sizes 4 or 10, meaning 6 different RNNs. Experimental results on DT recognition conducted on Dyntex++ and UCLA datasets show a high discriminatory power of our descriptor, providing an accuracy of 99.92%, 98.19%, 98.94% and 95.03% on the UCLA-50, UCLA-9, UCLA-8 and Dyntex++ databases, respectively. These results outperform various literature approaches, particularly for UCLA-50. More significantly, our method is competitive in terms of computational efficiency and descriptor size. It is therefore a good option for real-time dynamic texture segmentation, as illustrated by experiments conducted on videos acquired from a moving boat. (C) 2021 Published by Elsevier B.V. (AU)

• Processo FAPESP: 16/23763-8 - Modelagem e análise de redes complexas para visão computacional
• Beneficiário: Lucas Correia Ribas
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 18/22214-6 - Rumo à convergência de tecnologias: de sensores e biossensores à visualização de informação e aprendizado de máquina para análise de dados em diagnóstico clínico
• Beneficiário: Osvaldo Novais de Oliveira Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/18809-9 - Deep learning e redes complexas aplicados em visão computacional
• Beneficiário: Odemir Martinez Bruno
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 14/08026-1 - Visão artificial e reconhecimento de padrões aplicados em plasticidade vegetal
• Beneficiário: Odemir Martinez Bruno
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 19/03277-0 - Reconhecimento de Padrões em Redes Complexas usando Transformada da Distância
• Beneficiário: Lucas Correia Ribas
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado