An enhanced 3DCNN‐ConvLSTM for spatiotemporal multimedia data analysis

2019 ◽  
Vol 33 (2) ◽  
Author(s):  
Tian Wang ◽  
Jiakun Li ◽  
Mengyi Zhang ◽  
Aichun Zhu ◽  
Hichem Snoussi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multimedia Data

The interaction between multimedia data analysis and theory development in design research

2010 ◽  
Vol 22 (1) ◽  
pp. 6-30 ◽  
Author(s):  
Fenna van Nes ◽  
Michiel Doorman
Keyword(s):  
Data Analysis ◽  
Design Research ◽  
Theory Development ◽  
Multimedia Data

scholarly journals Optimal multimodal fusion for multimedia data analysis

2004 ◽  
Author(s):  
Yi Wu ◽  
Edward Y. Chang ◽  
Kevin Chen-Chuan Chang ◽  
John R. Smith
Keyword(s):  
Data Analysis ◽  
Multimedia Data ◽  
Multimodal Fusion

scholarly journals Inlier modeling for multimedia data analysis

2005 ◽  
Author(s):  
R. Dahyot ◽  
N. Rea ◽  
A. Kokaram ◽  
N. Kingsbury
Keyword(s):  
Data Analysis ◽  
Multimedia Data

scholarly journals Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shicheng Li ◽  
Qinghua Liu ◽  
Jiangyan Dai ◽  
Wenle Wang ◽  
Xiaolin Gui ◽  
...  
Keyword(s):  
Data Analysis ◽  
Matrix Factorization ◽  
Feature Learning ◽  
Representation Learning ◽  
Feature Representation ◽  
Multimedia Data ◽  
Basis Matrix ◽  
Rich Information ◽  
Low Dimensional ◽  
Better Than

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

A Fuzzy Adaptive Dynamic NSGA-II With Fuzzy-Based Borda Ranking Method and its Application to Multimedia Data Analysis

2021 ◽  
Vol 29 (1) ◽  
pp. 118-128
Author(s):  
Maysam Orouskhani ◽  
Daming Shi ◽  
Xiaochun Cheng
Keyword(s):  
Data Analysis ◽  
Ranking Method ◽  
Multimedia Data ◽  
Nsga Ii ◽  
Adaptive Dynamic ◽  
Fuzzy Adaptive

Functional and QoS Semantics-Driven SOA-Based Biomedical Multimedia Processing

2011 ◽  
pp. 335-359
Author(s):  
Shih-Hsi Liu ◽  
Yu Cao ◽  
Ming Li ◽  
Thell Smith ◽  
John Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Education ◽  
Feature Extraction ◽  
Data Analysis ◽  
Requirements Elicitation ◽  
Multimedia Data ◽  
Multimedia Processing ◽  
Wide Range ◽  
Processing Component

Although there have existed a wide range of techniques of biomedical multimedia processing, none of them could be generally satisfied by various domains. The main reason for such deficiency is due to the correlative nature between biomedical multimedia data and the techniques applied to them. This book chapter introduces an SOA-based biomedical multimedia infrastructure with a pre-processing component. Such an infrastructure adapts the concepts of requirements elicitation of Software Engineering as well as a training set of Machine Learning to analyze functional and QoS properties of biomedical multimedia data in advance. Such properties will be constructed as ontology and used for selecting the most appropriate services to perform data analysis, transmission, or retrieval. Two medical education projects are introduced as case studies to illustrate the usage of functional and QoS semantics extracted from a feature extraction service to improve the performance of subsequent classification service and searching service, respectively.

Intelligent Multimedia Data Analysis

2019 ◽  
Author(s):  
Dibya Jyoti Bora ◽  
Sebastian Salazar-Colores ◽  
Fernando Cervantes-Sanchez ◽  
Arturo Hernandez-Aguirre ◽  
Ivan Cruz-Aceves ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multimedia Data ◽  
Intelligent Multimedia

scholarly journals Multi-platform multimedia data analysis and modeling

2020 ◽  
Vol 79 (45-46) ◽  
pp. 33331-33331
Keyword(s):  
Data Analysis ◽  
Multimedia Data
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