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Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert

Grant number: 17/25908-6
Support Opportunities:Research Grants - Research Partnership for Technological Innovation - PITE
Duration: February 01, 2019 - January 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Convênio/Acordo: Microsoft Research
Principal Investigator:João Paulo Papa
Grantee:João Paulo Papa
Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Host Company: Microsoft Informática Ltda
City: Rio ClaroSão Paulo
Pesquisadores principais:
( Últimos )
Daniel Carlos Guimarães Pedronette ; Fabio Augusto Faria ; Jurandy Gomes de Almeida Junior
Pesquisadores principais:
( Antigos )
João Paulo Papa
Associated researchers:João Paulo Papa
Associated scholarship(s):22/01246-2 - A comparative analysis of depth feature for multimedia recognition tasks, BP.IC
21/10048-7 - Support for computational environments and experiments execution: data acquisition, categorization, and maintenance, BP.TT
21/10547-3 - Investigation of multi-level representations in multimedia recognition tasks, BP.IC
+ associated scholarships 21/02023-4 - Classifier Selection Strategies based on Genetic Programming for Multimedia Recognition, BP.MS
21/02739-0 - Visual attention models based on compressed domain video analysis techniques, BP.MS
21/01870-5 - Multi-level Representation Fusion Methods based on Weakly Supervised Learning, BP.MS
20/11366-0 - Support for computational environments and experiments execution: weakly-supervised and classification fusion methods, BP.TT
20/12101-0 - Support for computational environments and experiments execution: data acquisition, categorization and maintenance, BP.TT
20/08770-3 - Open set methods based on deep networks for multimedia recognition, BP.MS
20/08854-2 - Investigation of graph-based contextual measures for weakly-supervised learning, BP.IC
19/11104-8 - A comparative analysis of rank correlation measures for weakly-supervised learning, BP.IC
19/15837-0 - Restricted Boltzmann Machines applied to video-based action recognition, BP.IC
19/10998-5 - Investigation of compressed domain video features based on deep neural networks, BP.IC
19/07825-1 - Deep Boltzmann machines for event recognition in videos, BP.MS
19/04754-6 - Weakly supervised learning strategies through Rank-based measures, BP.MS - associated scholarships

Abstract

Several machine learning techniques have relied on large labeled data sets to construct predictive models and solving supervised learning tasks. The use of deep learning techniques can be highlighted, since it have been broadly and successfully used in various domains. On the other hand, in many circumstances, the labeled sets are unavailable or insufficient to train effective supervised models. Such scenarios have been mainly addressed by unsupervised learning techniques, which consider the unlabeled data to learn about its structure. However, the use of completely unsupervised methods still remains a research challenge in many scenarios and situations. A promising solution is based on the use of weakly supervised approaches, capable of performing effective learning tasks based on incomplete or inaccurate labeled sets. In this project, we intend to investigate the analysis, retrieval, and classification of compressed video domain based on small training sets. The main object of the project consists in to investigate and propose methods capable of analyzing compressed video sequences and trigger alerts according to considered applications. Such approaches can be useful and relevant in several domains, ranging from surveillance, medical and industrial environments to smart homes. The fundamental research challenge consists in making use of different techniques in order to analyze, represent, and classification videos using restricted labeled data. The proposed approach aims at exploiting the maximum available information, in order to become the approach suitable for operating with small training datasets. We intend to exploit: (i) deep learning representations; (ii) contextual unsupervised measures and; (iii) fusion techniques, in order to extend the initial labeled sets. The first challenge to be addressed is to analyze and represent videos in the compressed domain using deep learning techniques. Based on such representations, we intend to investigate strategies for expanding the training sets using unsupervised contextual measures. Given the obtained labeled sets, fusion strategies will be used to combined diverse classification methods and triggering alerts. Although the methods which will investigated can be used in several domains, we intend to select domains to validate the proposed approaches. The selection will be performed considering the existence of public available datasets to conduct experimental evaluations. (AU)

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Scientific publications (47)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
RODER, MATEUS; PASSOS, LEANDRO APARECIDO; DE ROSA, GUSTAVO H.; DE ALBUQUERQUE, VICTOR HUGO C.; PAPA, JOAO PAULO. Reinforcing learning in Deep Belief Networks through nature-inspired optimization. APPLIED SOFT COMPUTING, v. 108, . (19/07825-1, 18/21934-5, 17/25908-6, 19/07665-4, 19/02205-5, 13/07375-0, 14/12236-1)
ASCENCAO, NATHALIA Q.; AFONSO, LUIS C. S.; COLOMBO, DANILO; OLIVEIRA, LUCIANO; PAPA, JOAO P.; IEEE. Information Ranking Using Optimum-Path Forest. 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., . (18/15597-6, 14/12236-1, 19/07665-4, 13/07375-0, 17/25908-6)
FERREIRA, ALVARO R., JR.; DE ROSA, GUSTAVO H.; PAPA, JOAO P.; CARNEIRO, GUSTAVO; FARIA, FABIO A.; IEEE COMP SOC. Creating Classifier Ensembles through Meta-heuristic Algorithms for Aerial Scene Classification. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 8-pg., . (14/12236-1, 18/23908-1, 17/25908-6, 19/07665-4, 19/02205-5)
RODER, MATEUS; DE ROSA, GUSTAVO HENRIQUE; PAPA, JOAO PAULO; BREVE, FABRICIO APARECIDO; IEEE. Fine-Tuning Temperatures in Restricted Boltzmann Machines Using Meta-Heuristic Optimization. 2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), v. N/A, p. 8-pg., . (19/02205-5, 17/25908-6, 19/07825-1, 13/07375-0, 14/12236-1)
SUGI AFONSO, LUIS CLAUDIO; RODRIGUES, DOUGLAS; PAPA, JOAO PAULO. Nature-inspired optimum-path forest. EVOLUTIONARY INTELLIGENCE, . (17/25908-6, 18/21934-5, 14/12236-1, 13/07375-0, 19/07665-4)
ROZIN, BIONDA; PEREIRA-FERRERO, VANESSA HELENA; LOPES, LEONARDO TADEU; PEDRONETTE, DANIEL CARLOS GUIMARAES. A rank-based framework through manifold learning for improved clustering tasks. INFORMATION SCIENCES, v. 580, p. 202-220, . (17/25908-6, 20/02183-9, 20/08854-2, 18/15597-6)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS. Unsupervised selective rank fusion for image retrieval tasks. Neurocomputing, v. 377, p. 182-199, . (17/25908-6, 17/02091-4, 18/15597-6)
DE ROSA, GUSTAVO H.; PAPA, JOAO P.; YANG, XIN-SHE. A nature-inspired feature selection approach based on hypercomplex information. APPLIED SOFT COMPUTING, v. 94, . (13/07375-0, 16/19403-6, 14/12236-1, 17/25908-6, 17/02286-0, 19/02205-5)
RODER, MATEUS; DE ROSA, GUSTAVO HENRIQUE; PASSOS, LEANDRO APARECIDO; PAPA, JOAO PAULO; DEBIASO ROSSI, ANDRE LUIS; IEEE. Harnessing Particle Swarm Optimization Through Relativistic Velocity. 2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), v. N/A, p. 8-pg., . (19/02205-5, 17/25908-6, 19/07825-1, 13/07375-0, 19/07665-4, 14/12236-1)
GUIMARAES PEDRONETTE, DANIEL CARLOS; PASCOTTI VALEM, LUCAS; LATECKI, LONGIN JAN. Efficient Rank-Based Diffusion Process with Assured Convergence. JOURNAL OF IMAGING, v. 7, n. 3, . (20/11366-0, 18/15597-6, 17/25908-6)
FARIA, FABIO AUGUSTO; CARNEIRO, GUSTAVO; IEEE. Why are Generative Adversarial Networks so Fascinating and Annoying?. 2020 33RD SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2020), v. N/A, p. 8-pg., . (18/23908-1, 17/25908-6)
RIBEIRO, LUIZ C. F.; DE ROSA, GUSTAVO H.; RODRIGUES, DOUGLAS; PAPA, JOAO P.. Convolutional neural networks ensembles through single-iteration optimization. SOFT COMPUTING, v. 26, n. 8, p. 12-pg., . (14/12236-1, 19/07665-4, 13/07375-0, 17/25908-6, 19/02205-5, 18/21934-5)
RODER, MATEUS; ALMEIDA, JURANDY; DE ROSA, GUSTAVO H.; PASSOS, LEANDRO A.; ROSSI, ANDRE L. D.; PAPA, JOAO P.; IEEE. From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks. 2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021), v. N/A, p. 8-pg., . (13/07375-0, 19/07825-1, 17/25908-6, 19/07665-4)
NETO, VICENTE COELHO LOBO; PASSOS, LEANDRO APARECIDO; PAPA, JOAO PAULO; KRZHIZHANOVSKAYA, VV; ZAVODSZKY, G; LEES, MH; DONGARRA, JJ; SLOOT, PMA; BRISSOS, S; TEIXEIRA, J. Evolving Long Short-Term Memory Networks. COMPUTATIONAL SCIENCE - ICCS 2020, PT II, v. 12138, p. 14-pg., . (17/25908-6, 18/10100-6, 13/07375-0, 14/12236-1, 19/07665-4)
DE SA, NIKOLAS GOMES; VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; FARINELLA, GM; RADEVA, P; BRAZ, J; BOUATOUCH, K. A Multi-level Rank Correlation Measure for Image Retrieval. VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP, v. N/A, p. 9-pg., . (17/25908-6, 18/15597-6, 19/11104-8)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS. Graph -based selective rank fusion for unsupervised image retrieval. PATTERN RECOGNITION LETTERS, v. 135, p. 82-89, . (17/25908-6, 13/08645-0, 17/02091-4, 18/15597-6)
GUIMARAES PEDRONETTE, DANIEL CARLOS; LATECKI, LONGIN JAN. Rank-based self-training for graph convolutional networks. INFORMATION PROCESSING & MANAGEMENT, v. 58, n. 2, . (17/25908-6, 18/15597-6)
DE FERNANDO, FILIPE ALVES; GUIMARAES PEDRONETTE, DANIEL CARLOS; DE SOUSA, GUSTAVO JOSE; VALEM, LUCAS PASCOTTI; GUILHERME, IVAN RIZZO; FARINELLA, GM; RADEVA, P; BRAZ, J. RaDE: A Rank-based Graph Embedding Approach. VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP, v. N/A, p. 11-pg., . (17/25908-6, 18/15597-6)
LOPES, LEONARDO TADEU; GUIMARAES PEDRONETTE, DANIEL CARLOS; IEEE. Self-Supervised Clustering based on Manifold Learning and Graph Convolutional Networks. 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), v. N/A, p. 10-pg., . (18/15597-6, 17/25908-6)
GONCALVES, FILIPE MARCEL FERNANDES; PEDRONETTE, DANIEL CARLOS GUIMARAES; TORRES, RICARDO DA SILVA. Regression by Re-Ranking. PATTERN RECOGNITION, v. 140, p. 17-pg., . (13/50155-0, 18/15597-6, 14/12236-1, 16/50250-1, 15/24494-8, 17/25908-6, 17/20945-0)
PIMENTA, GUILHERME B. A.; DALLAQUA, FERNANDA B. J. R.; FAZENDA, ALVARO; FARIA, FABIO A.; DECARVALHO, BM; GONCALVES, LMG. Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests. 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022), v. N/A, p. 6-pg., . (19/26702-8, 15/24485-9, 14/50937-1, 18/23908-1, 17/25908-6)
DOS SANTOS, SAMUEL FELIPE; SEBE, NICU; ALMEIDA, JURANDY; IEEE. THE GOOD, THE BAD, AND THE UGLY: NEURAL NETWORKS STRAIGHT FROM JPEG. 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), v. N/A, p. 5-pg., . (18/21837-0, 17/25908-6)
CAMPOS, VICTOR DE ABREU; GUIMARAES PEDRONETTE, DANIEL CARLOS. A framework for speaker retrieval and identification through unsupervised learning. COMPUTER SPEECH AND LANGUAGE, v. 58, p. 153-174, . (17/25908-6, 15/07934-4, 18/15597-6)
GUIMARAES PEDRONETTE, DANIEL CARLOS; VALEM, LUCAS PASCOTTI; ALMEIDA, JURANDY; TONES, RICARDO DA S.. Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking. IEEE Transactions on Image Processing, v. 28, n. 12, p. 5824-5838, . (14/50715-9, 16/50250-1, 17/25908-6, 17/20945-0, 14/12236-1, 16/06441-7, 18/15597-6, 13/50155-0, 17/02091-4, 15/24494-8)
GUIMARAES PEDRONETTE, DANIEL CARLOS; WENG, YING; BALDASSIN, ALEXANDRO; HOU, CHAOHUAN. Semi-supervised and active learning through Manifold Reciprocal kNN Graph for image retrieval. Neurocomputing, v. 340, p. 19-31, . (17/25908-6, 13/08645-0)
SILVA, LUCAS F. A.; PEDRONETTE, DANIEL C. G.; FARIA, FABIO A.; PAPA, JOAO P.; ALMEIDA, JURANDY; IEEE COMP SOC. Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers. 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021), v. N/A, p. 8-pg., . (14/12236-1, 18/15597-6, 17/25908-6, 19/07665-4, 13/07375-0)
CAMACHO PRESOTTO, JOAO GABRIEL; VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL CARLOS GUIMARAES; VENTO, M; PERCANNELLA, G. Unsupervised Effectiveness Estimation Through Intersection of Ranking References. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT II, v. 11679, p. 14-pg., . (18/15597-6, 17/25908-6, 17/02091-4)
VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL CARLOS GUIMARAES; LATECKI, LONGIN JAN. Graph Convolutional Networks based on manifold learning for semi-supervised image classification. COMPUTER VISION AND IMAGE UNDERSTANDING, v. 227, p. 14-pg., . (17/25908-6, 18/15597-6, 20/11366-0)
GONCALVES DOS SANTOS, CLAUDIO FILIPI; MOREIRA, THIERRY PINHEIRO; COLOMBO, DANILO; PAPA, JOAO PAULO; NYSTROM, I; HEREDIA, YH; NUNEZ, VM. Does Pooling Really Matter? An Evaluation on Gait Recognition. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS (CIARP 2019), v. 11896, p. 10-pg., . (16/06441-7, 17/25908-6, 14/12236-1, 13/07375-0)
DOS SANTOS, SAMUEL FELIPE; SEBE, NICU; ALMEIDA, JURANDY; IEEE. CV-C3D: Action Recognition on Compressed Videos with Convolutional 3D Networks. 2019 32ND SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 7-pg., . (17/25908-6, 18/21837-0)
GONCALVES DO SANTOS, CLAUDIO FILIPI; COLOMBO, DANILO; RODER, MATEUS; PAPA, JOAO PAULO; IEEE COMP SOC. MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 6-pg., . (14/12236-1, 19/07825-1, 13/07375-0, 17/25908-6)
FONTINELE, JEFFERSON; MENDONCA, MARCELO; RUIZ, MARCO; PAPA, JOAO; OLIVEIRA, LUCIANO; IEEE. Faster alpha-expansion via dynamic programming and image partitioning. 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., . (13/07375-0, 14/12236-1, 17/25908-6)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ACM. A Denoising Convolutional Neural Network for Self-Supervised Rank Effectiveness Estimation on Image Retrieval. PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR '21), v. N/A, p. 9-pg., . (18/15597-6, 17/25908-6, 20/11366-0)
VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL CARLOS GUIMARAES. Person Re-ID through unsupervised hypergraph rank selection and fusion. Image and Vision Computing, v. 123, p. 14-pg., . (18/15597-6, 17/25908-6)
VALEM, LUCAS PASCOTTI; GUIMARDES PEDRONETTE, DANIEL CARLOS; ASSOC COMP MACHINERY. An Unsupervised Genetic Algorithm Framework for Rank Selection and Fusion on Image Retrieval. ICMR'19: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, v. N/A, p. 5-pg., . (18/15597-6, 17/25908-6, 17/02091-4)
DE ALMEIDA, LUCAS BARBOSA; PEREIRA-FERRERO, VANESSA HELENA; VALEM, LUCAS PASCOTTI; ALMEIDA, JURANDY; GUIMARAES PEDRONETTE, DANIEL CARLOS; IEEE COMP SOC. Representation Learning for Image Retrieval through 3D CNN and Manifold Ranking. 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021), v. N/A, p. 8-pg., . (18/15597-6, 20/02183-9, 17/25908-6)
SCARPARO, DANIELE CRISTINA; PINHEIRO SALVADEO, DENIS HENRIQUE; GUIMARAES PEDRONETTE, DANIEL CARLOS; BARUFALDI, BRUNO; ARNOLD MAIDMENT, ANDREW DOUGLAS. Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain. JOURNAL OF MEDICAL IMAGING, v. 6, n. 3, . (16/09714-4, 17/17811-2, 17/25908-6)
DE SOUZA, RENATO WILLIAM R.; DE OLIVEIRA, JOAO VITOR CHAVES; PASSOS, JR., LEANDRO A.; DING, WEIPING; PAPA, JOAO P.; DE ALBUQUERQUE, VICTOR HUGO C.. A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic. IEEE TRANSACTIONS ON FUZZY SYSTEMS, v. 28, n. 12, p. 3076-3086, . (13/07375-0, 17/25908-6, 18/21934-5, 16/19403-6, 14/12236-1)
GUIMARAES PEDRONETTE, DANIEL CARLOS; VALEM, LUCAS PASCOTTI; TORRES, RICARDO DA S.. A BFS-Tree of ranking references for unsupervised manifold learning. PATTERN RECOGNITION, v. 111, . (16/50250-1, 15/24494-8, 13/50155-0, 18/15597-6, 13/50169-1, 17/20945-0, 14/12236-1, 17/25908-6, 14/50715-9)
SANTANA, MARCOS C. S.; PASSOS, JR., LEANDRO APARECIDO; MOREIRA, THIERRY P.; COLOMBO, DANILO; DE ALBUQUERQUE, VICTOR HUGO C.; PAPA, JOAO PAULO. A Novel Siamese-Based Approach for Scene Change Detection With Applications to Obstructed Routes in Hazardous Environments. IEEE INTELLIGENT SYSTEMS, v. 35, n. 1, p. 44-53, . (14/12236-1, 13/07375-0, 17/25908-6, 16/19403-6)
DOS SANTOS, SAMUEL FELIPE; ALMEIDA, JURANDY; IEEE. Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain. 2020 33RD SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2020), v. N/A, p. 7-pg., . (17/25908-6)
VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL CARLOS GUIMARAES; LATECKI, LONGIN JAN. Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning. IEEE Transactions on Image Processing, v. 32, p. 16-pg., . (18/15597-6, 17/25908-6)
DE SOUZA MIRANDA, MATEUS; ALVARENGA E SILVA, LUCAS FERNANDO; DOS SANTOS, SAMUEL FELIPE; DE SANTIAGO JUNIOR, VALDIVINO ALEXANDRE; KORTING, THALES SEHN; ALMEIDA, JURANDY; DECARVALHO, BM; GONCALVES, LMG. A High-Spatial Resolution Dataset and Few-shot Deep Learning Benchmark for Image Classification. 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022), v. N/A, p. 6-pg., . (17/25908-6, 20/08770-3)
PRESOTTO, JOAO GABRIEL CAMACHO; DOS SANTOS, SAMUEL FELIPE; VALEM, LUCAS PASCOTTI; FARIA, FABIO AUGUSTO; PAPA, JOAO PAULO; ALMEIDA, JURANDY; PEDRONETTE, DANIEL CARLOS GUIMARAES. Weakly supervised learning based on hypergraph manifold ranking?. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v. 89, p. 12-pg., . (18/15597-6, 18/23908-1, 17/25908-6, 19/04754-6, 20/11366-0)
GONCALVES DOS SANTOS, CLAUDIO FILIPI; PAPA, JOAO PAULO. Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks. ACM COMPUTING SURVEYS, v. 54, n. 10S, p. 25-pg., . (14/12236-1, 17/25908-6)
PEREIRA-FERRERO, VANESSA HELENA; VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL CARLOS GUIMARAES. Feature augmentation based on manifold ranking and LSTM for image classification (R). EXPERT SYSTEMS WITH APPLICATIONS, v. 213, p. 16-pg., . (17/25908-6, 20/02183-9, 18/15597-6, 20/11366-0)
LOPES, LEONARDO TADEU; VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; GUILHERME, IVAN RIZZO; PAPA, JOAO PAULO; SILVA SANTANA, MARCOS CLEISON; COLOMBO, DANILO; FARINELLA, GM; RADEVA, P; BRAZ, J. Manifold Learning-based Clustering Approach Applied to Anomaly Detection in Surveillance Videos. VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP, v. N/A, p. 9-pg., . (18/15597-6, 14/12236-1, 13/07375-0, 17/25908-6, 19/07825-1, 18/21934-5)

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