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2018 1st International Conference on Multimedia Analysis and Pattern Recognition : 5-6 April 2018, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers
| 2018 1st International Conference on Multimedia Analysis and Pattern Recognition : 5-6 April 2018, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (20 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Multimedia data mining
Pattern recognition systems |
| ISBN | 1-5386-4180-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996280698903316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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2018 1st International Conference on Multimedia Analysis and Pattern Recognition : 5-6 April 2018, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers
| 2018 1st International Conference on Multimedia Analysis and Pattern Recognition : 5-6 April 2018, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (20 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Multimedia data mining
Pattern recognition systems |
| ISBN | 1-5386-4180-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910280906203321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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2019 International Conference on Multimedia Analysis and Pattern Recognition : 9-10 May 2019, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers
| 2019 International Conference on Multimedia Analysis and Pattern Recognition : 9-10 May 2019, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2019 |
| Descrizione fisica | 1 online resource (20 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Multimedia data mining
Pattern recognition systems |
| ISBN | 1-7281-1829-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910330357903321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
2019 International Conference on Multimedia Analysis and Pattern Recognition : 9-10 May 2019, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers
| 2019 International Conference on Multimedia Analysis and Pattern Recognition : 9-10 May 2019, Ho Chi Minh City, Vietnam / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2019 |
| Descrizione fisica | 1 online resource (20 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Multimedia data mining
Pattern recognition systems |
| ISBN | 1-7281-1829-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996575572303316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) / / Institute of Electrical and Electronics Engineers (IEEE)
| 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) / / Institute of Electrical and Electronics Engineers (IEEE) |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 |
| Descrizione fisica | 1 online resource : illustrations some color |
| Disciplina | 006.3 |
| Soggetto topico |
Multimedia data mining
Conference papers and proceedings Pattern recognition systems |
| ISBN | 1-7281-6555-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2020 International Conference on Multimedia Analysis and Pattern Recognition |
| Record Nr. | UNINA-9910437240303321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) / / Institute of Electrical and Electronics Engineers (IEEE)
| 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) / / Institute of Electrical and Electronics Engineers (IEEE) |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 |
| Descrizione fisica | 1 online resource : illustrations some color |
| Disciplina | 006.3 |
| Soggetto topico |
Multimedia data mining
Conference papers and proceedings Pattern recognition systems |
| ISBN | 1-7281-6555-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2020 International Conference on Multimedia Analysis and Pattern Recognition |
| Record Nr. | UNISA-996575552003316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
| Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others] |
| Autore | Vrochidis Stefanos <1975-> |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 005.7 |
| Collana | THEi Wiley ebooks. |
| Soggetto topico |
Multimedia data mining
Big data |
| ISBN |
1-119-37699-8
1-119-37698-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910544580603321 |
Vrochidis Stefanos <1975->
|
||
| Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others]
| Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Multimedia data mining
Big data |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-37699-8
1-119-37698-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467073603321 |
| Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis ... [et al.]
| Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2019 |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 005.7 |
| Collana | THEi Wiley ebooks. |
| Soggetto topico |
Multimedia data mining
Big data |
| ISBN |
1-119-37699-8
1-119-37698-X 9781119376989 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Introduction -- List of Contributors -- About the Companion Website -- Part I Feature Extraction from Big Multimedia Data -- Chapter 1 Representation Learning on Large and Small Data -- 1.1 Introduction -- 1.2 Representative Deep CNNs -- 1.2.1 AlexNet -- 1.2.1.1 ReLU Nonlinearity -- 1.2.1.2 Data Augmentation -- 1.2.1.3 Dropout -- 1.2.2 Network in Network -- 1.2.2.1 MLP Convolutional Layer -- 1.2.2.2 Global Average Pooling -- 1.2.3 VGG -- 1.2.3.1 Very Small Convolutional Filters -- 1.2.3.2 Multi‐scale Training -- 1.2.4 GoogLeNet -- 1.2.4.1 Inception Modules -- 1.2.4.2 Dimension Reduction -- 1.2.5 ResNet -- 1.2.5.1 Residual Learning -- 1.2.5.2 Identity Mapping by Shortcuts -- 1.2.6 Observations and Remarks -- 1.3 Transfer Representation Learning -- 1.3.1 Method Specifications -- 1.3.2 Experimental Results and Discussion -- 1.3.2.1 Results of Transfer Representation Learning for OM -- 1.3.2.2 Results of Transfer Representation Learning for Melanoma -- 1.3.2.3 Qualitative Evaluation: Visualization -- 1.3.3 Observations and Remarks -- 1.4 Conclusions -- References -- Chapter 2 Concept‐Based and Event‐Based Video Search in Large Video Collections -- 2.1 Introduction -- 2.2 Video preprocessing and Machine Learning Essentials -- 2.2.1 Video Representation -- 2.2.2 Dimensionality Reduction -- 2.3 Methodology for Concept Detection and Concept‐Based Video Search -- 2.3.1 Related Work -- 2.3.2 Cascades for Combining Different Video Representations -- 2.3.2.1 Problem Definition and Search Space -- 2.3.2.2 Problem Solution -- 2.3.3 Multi‐Task Learning for Concept Detection and Concept‐Based Video Search -- 2.3.4 Exploiting Label Relations -- 2.3.5 Experimental Study -- 2.3.5.1 Dataset and Experimental Setup -- 2.3.5.2 Experimental Results -- 2.3.5.3 Computational Complexity.
2.4 Methods for Event Detection and Event‐Based Video Search -- 2.4.1 Related Work -- 2.4.2 Learning from Positive Examples -- 2.4.3 Learning Solely from Textual Descriptors: Zero‐Example Learning -- 2.4.4 Experimental Study -- 2.4.4.1 Dataset and Experimental Setup -- 2.4.4.2 Experimental Results: Learning from Positive Examples -- 2.4.4.3 Experimental Results: Zero‐Example Learning -- 2.5 Conclusions -- 2.6 Acknowledgments -- References -- Chapter 3 Big Data Multimedia Mining: Feature Extraction Facing Volume, Velocity, and Variety -- 3.1 Introduction -- 3.2 Scalability through Parallelization -- 3.2.1 Process Parallelization -- 3.2.2 Data Parallelization -- 3.3 Scalability through Feature Engineering -- 3.3.1 Feature Reduction through Spatial Transformations -- 3.3.2 Laplacian Matrix Representation -- 3.3.3 Parallel latent Dirichlet allocation and bag of words -- 3.4 Deep Learning‐Based Feature Learning -- 3.4.1 Adaptability that Conquers both Volume and Velocity -- 3.4.2 Convolutional Neural Networks -- 3.4.3 Recurrent Neural Networks -- 3.4.4 Modular Approach to Scalability -- 3.5 Benchmark Studies -- 3.5.1 Dataset -- 3.5.2 Spectrogram Creation -- 3.5.3 CNN‐Based Feature Extraction -- 3.5.4 Structure of the CNNs -- 3.5.5 Process Parallelization -- 3.5.6 Results -- 3.6 Closing Remarks -- 3.7 Acknowledgements -- References -- Part II Learning Algorithms for Large-Scale Multimedia -- Chapter 4 Large‐Scale Video Understanding with Limited Training Labels -- 4.1 Introduction -- 4.2 Video Retrieval with Hashing -- 4.2.1 Overview -- 4.2.2 Unsupervised Multiple Feature Hashing -- 4.2.2.1 Framework -- 4.2.2.2 The Objective Function of MFH -- 4.2.2.3 Solution of MFH -- 4.2.2.3.1 Complexity Analysis -- 4.2.3 Submodular Video Hashing -- 4.2.3.1 Framework -- 4.2.3.2 Video Pooling -- 4.2.3.3 Submodular Video Hashing -- 4.2.4 Experiments. 4.2.4.1 Experiment Settings -- 4.2.4.1.1 Video Datasets -- 4.2.4.1.2 Visual Features -- 4.2.4.1.3 Algorithms for Comparison -- 4.2.4.2 Results -- 4.2.4.2.1 CC_WEB_VIDEO -- 4.2.4.2.2 Combined Dataset -- 4.2.4.3 Evaluation of SVH -- 4.2.4.3.1 Results -- 4.3 Graph‐Based Model for Video Understanding -- 4.3.1 Overview -- 4.3.2 Optimized Graph Learning for Video Annotation -- 4.3.2.1 Framework -- 4.3.2.2 OGL -- 4.3.2.2.1 Terms and Notations -- 4.3.2.2.2 Optimal Graph-Based SSL -- 4.3.2.2.3 Iterative Optimization -- 4.3.3 Context Association Model for Action Recognition -- 4.3.3.1 Context Memory -- 4.3.4 Graph‐based Event Video Summarization -- 4.3.4.1 Framework -- 4.3.4.2 Temporal Alignment -- 4.3.5 TGIF: A New Dataset and Benchmark on Animated GIF Description -- 4.3.5.1 Data Collection -- 4.3.5.2 Data Annotation -- 4.3.6 Experiments -- 4.3.6.1 Experimental Settings -- 4.3.6.2 Results -- 4.4 Conclusions and Future Work -- References -- Chapter 5 Multimodal Fusion of Big Multimedia Data -- 5.1 Multimodal Fusion in Multimedia Retrieval -- 5.1.1 Unsupervised Fusion in Multimedia Retrieval -- 5.1.1.1 Linear and Non‐linear Similarity Fusion -- 5.1.1.2 Cross‐modal Fusion of Similarities -- 5.1.1.3 Random Walks and Graph‐based Fusion -- 5.1.1.4 A Unifying Graph‐based Model -- 5.1.2 Partial Least Squares Regression -- 5.1.3 Experimental Comparison -- 5.1.3.1 Dataset Description -- 5.1.3.2 Settings -- 5.1.3.3 Results -- 5.1.4 Late Fusion of Multiple Multimedia Rankings -- 5.1.4.1 Score Fusion -- 5.1.4.2 Rank Fusion -- 5.1.4.2.1 Borda Count Fusion -- 5.1.4.2.2 Reciprocal Rank Fusion -- 5.1.4.2.3 Condorcet Fusion -- 5.2 Multimodal Fusion in Multimedia Classification -- 5.2.1 Related Literature -- 5.2.2 Problem Formulation -- 5.2.3 Probabilistic Fusion in Active Learning -- 5.2.3.1 If P(S& -- equals -- 0|V,T)≠0: -- 5.2.3.2 If P(S& -- equals -- 0|V,T)&. equals -- 0: -- 5.2.3.3 Incorporating Informativeness in the Selection (P(S|V)) -- 5.2.3.4 Measuring Oracle's Confidence (P(S|T)) -- 5.2.3.5 Re‐training -- 5.2.4 Experimental Comparison -- 5.2.4.1 Datasets -- 5.2.4.2 Settings -- 5.2.4.3 Results -- 5.2.4.3.1 Expanding with Positive, Negative or Both -- 5.2.4.3.2 Comparing with Sample Selection Approaches -- 5.2.4.3.3 Comparing with Fusion Approaches -- 5.2.4.3.4 Parameter Sensitivity Investigation -- 5.2.4.3.5 Comparing with Existing Methods -- 5.3 Conclusions -- References -- Chapter 6 Large‐Scale Social Multimedia Analysis -- 6.1 Social Multimedia in Social Media Streams -- 6.1.1 Social Multimedia -- 6.1.2 Social Multimedia Streams -- 6.1.3 Analysis of the Twitter Firehose -- 6.1.3.1 Dataset: Overview -- 6.1.3.2 Linked Resource Analysis -- 6.1.3.3 Image Content Analysis -- 6.1.3.4 Geographic Analysis -- 6.1.3.5 Textual Analysis -- 6.2 Large‐Scale Analysis of Social Multimedia -- 6.2.1 Large‐Scale Processing of Social Multimedia Analysis -- 6.2.1.1 Batch‐Processing Frameworks -- 6.2.1.2 Stream‐Processing Frameworks -- 6.2.1.3 Distributed Processing Frameworks -- 6.2.2 Analysis of Social Multimedia -- 6.2.2.1 Analysis of Visual Content -- 6.2.2.2 Analysis of Textual Content -- 6.2.2.3 Analysis of Geographical Content -- 6.2.2.4 Analysis of User Content -- 6.3 Large‐Scale Multimedia Opinion Mining System -- 6.3.1 System Overview -- 6.3.2 Implementation Details -- 6.3.2.1 Social Media Data Crawler -- 6.3.2.2 Social Multimedia Analysis -- 6.3.2.3 Analysis of Visual Content -- 6.3.3 Evaluations: Analysis of Visual Content -- 6.3.3.1 Filtering of Synthetic Images -- 6.3.3.2 Near‐Duplicate Detection -- 6.4 Conclusion -- References -- Chapter 7 Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger -- 7.1 Introduction -- 7.1.1 The Dark Side of Big Multimedia Data. 7.1.2 Defining Multimedia Privacy -- 7.2 Protecting User Privacy -- 7.2.1 What to Protect -- 7.2.2 How to Protect -- 7.2.3 Threat Models -- 7.3 Multimedia Privacy -- 7.3.1 Privacy and Multimedia Big Data -- 7.3.2 Privacy Threats of Multimedia Data -- 7.3.2.1 Audio Data -- 7.3.2.2 Visual Data -- 7.3.2.3 Multimodal Threats -- 7.4 Privacy‐Related Multimedia Analysis Research -- 7.4.1 Multimedia Analysis Filters -- 7.4.2 Multimedia Content Masking -- 7.5 The Larger Research Picture -- 7.5.1 Multimedia Security and Trust -- 7.5.2 Data Privacy -- 7.6 Outlook on Multimedia Privacy Challenges -- 7.6.1 Research Challenges -- 7.6.1.1 Multimedia Analysis -- 7.6.1.2 Data -- 7.6.1.3 Users -- 7.6.2 Research Reorientation -- 7.6.2.1 Professional Paranoia -- 7.6.2.2 Privacy as a Priority -- 7.6.2.3 Privacy in Parallel -- References -- Part III Scalability in Multimedia Access -- Chapter 8 Data Storage and Management for Big Multimedia -- 8.1 Introduction -- 8.1.1 Multimedia Applications and Scale -- 8.1.2 Big Data Management -- 8.1.3 System Architecture Outline -- 8.1.4 Metadata Storage Architecture -- 8.1.4.1 Lambda Architecture -- 8.1.4.2 Storage Layer -- 8.1.4.3 Processing Layer -- 8.1.4.4 Serving Layer -- 8.1.4.5 Dynamic Data -- 8.1.5 Summary and Chapter Outline -- 8.2 Media Storage -- 8.2.1 Storage Hierarchy -- 8.2.1.1 Secondary Storage -- 8.2.1.2 The Five‐Minute Rule -- 8.2.1.3 Emerging Trends for Local Storage -- 8.2.2 Distributed Storage -- 8.2.2.1 Distributed Hash Tables -- 8.2.2.2 The CAP Theorem and the PACELC Formulation -- 8.2.2.3 The Hadoop Distributed File System -- 8.2.2.4 Ceph -- 8.2.3 Discussion -- 8.3 Processing Media -- 8.3.1 Metadata Extraction -- 8.3.2 Batch Processing -- 8.3.2.1 Map‐Reduce and Hadoop -- 8.3.2.2 Spark -- 8.3.2.3 Comparison -- 8.3.3 Stream Processing -- 8.4 Multimedia Delivery -- 8.4.1 Distributed In‐Memory Buffering. 8.4.1.1 Memcached and Redis. |
| Altri titoli varianti | Big data analytics for large scale multimedia search |
| Record Nr. | UNINA-9910824409003321 |
| Hoboken, N.J., : Wiley, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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An introduction to web mining : with applications in R / / Ulrich Matter
| An introduction to web mining : with applications in R / / Ulrich Matter |
| Autore | Matter Ulrich |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , [2025] |
| Descrizione fisica | 1 online resource (xxi, 251 pages) : illustrations |
| Disciplina | 001.433 |
| Collana | Use R! |
| Soggetto topico |
Multimedia data mining
R (Computer program language) Social sciences - Statistical methods Statistics Methodology of Data Collection and Processing Data Mining and Knowledge Discovery Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy Statistics in Business, Management, Economics, Finance, Insurance Mineria de dades R (Llenguatge de programació) Estadística Metodologia de les ciències socials |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 9783031966385 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | - Part I: Context, Relevance, and the Basics -- 1. Introduction -- 2. The Internet as a Data Source -- Part II: Web Technologies and Automated Data Extraction -- 3. Web 1.0 Technologies: The Static Web -- 4. Web Scraping: Data Extraction from Websites -- 5. Web 2.0 Technologies: The Programmable/Dynamic Web -- 6. Extracting Data From The Programmable Web -- 7. Data Extraction from Dynamic Websites -- Part III: Advanced Topics in Web Mining -- 8. Web Mining Programs -- 9. Crawler Implementation -- 10. Appearance and Authentication -- 11. Scaling Web Mining in the Cloud -- 12. AI Tools for Web Mining: Overview and Outlook -- Part IV: Ethical, Legal, and Scientific Rigor -- 13. Ethics and Legal Considerations -- 14. Web Mining and Scientific Rigor. |
| Record Nr. | UNINA-9911020417803321 |
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Matter Ulrich
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| Cham : , : Springer, , [2025] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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