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Semantic Mining Technologies for Multimedia Databases

Release Date: April, 2009. Copyright © 2009. 550 pages.
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DOI: 10.4018/978-1-60566-188-9, ISBN13: 9781605661889, ISBN10: 1605661880, EISBN13: 9781605661896
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MLA

Tao, Dacheng , Dong Xu, and Xuelong Li. "Semantic Mining Technologies for Multimedia Databases." IGI Global, 2009. 1-550. Web. 15 Sep. 2014. doi:10.4018/978-1-60566-188-9

APA

Tao, D., Xu, D., & Li, X. (2009). Semantic Mining Technologies for Multimedia Databases (pp. 1-550). Hershey, PA: IGI Global. doi:10.4018/978-1-60566-188-9

Chicago

Tao, Dacheng , Dong Xu, and Xuelong Li. "Semantic Mining Technologies for Multimedia Databases." 1-550 (2009), accessed September 15, 2014. doi:10.4018/978-1-60566-188-9

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Description

Multimedia searching and management have become popular due to demanding applications and competition among companies. Despite the increase in interest, there is no existing book covering basic knowledge on state-of-the-art techniques within the field.

Semantic Mining Technologies for Multimedia Databases provides an introduction to the most recent techniques in multimedia semantic mining necessary to researchers new to the field. This book serves as an important reference in multimedia for academicians, multimedia technologists and researchers, and academic libraries.

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Table of Contents and List of Contributors

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Table of Contents
Preface
Dacheng Tao, Dong Xu, Xuelong Li
Chapter 1
Amr Ahmed
Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data... Sample PDF
Video Representation and Processing for Multimedia Data Mining
$37.50
Chapter 2
Sébastien Lefèvre
Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data... Sample PDF
Image Features from Morphological Scale-Spaces
$37.50
Chapter 3
Huiyu Zhou, Yuan Yuan, Chunmei Shi
The authors present a face recognition scheme based on semantic features’ extraction from faces and tensor subspace analysis. These semantic... Sample PDF
Face Recognition and Semantic Features
$37.50
Chapter 4
Haibin Ling, David W. Jacobs
Computer-aided foliage image retrieval systems have the potential to dramatically speed up the process of plant species identification. Despite... Sample PDF
Shape Matching for Foliage Database Retrieval
$37.50
Chapter 5
Shaohua Kevin Zhou, Jie Shao, Bogdan Georgescu, Dorin Comaniciu
Motion estimation necessitates an appropriate choice of similarity function. Because generic similarity functions derived from simple assumptions... Sample PDF
Similarity Learning for Motion Estimation
$37.50
Chapter 6
Jian Cheng, Kongqiao Wang, Hanqing Lu
Relevance feedback is an effective approach to boost the performance of image retrieval. Labeling data is indispensable for relevance feedback, but... Sample PDF
Active Learning for Relevance Feedback in Image Retrieval
$37.50
Chapter 7
Juliusz L. Kulikowski
Visual data mining is a procedure aimed at a selection from a document’s repository subsets of documents presenting certain classes of objects; the... Sample PDF
Visual Data Mining Based on Partial Similarity Concepts
$37.50
Chapter 8
Jinhui Tang, Xian-Sheng Hua, Meng Wang
The insufficiency of labeled training samples is a major obstacle in automatic semantic analysis of large scale image/video database.... Sample PDF
Image/Video Semantic Analysis by Semi-Supervised Learning
$37.50
Chapter 9
Shuqiang Jiang, Yonghong Tian, Qingming Huang, Tiejun Huang, Wen Gao
With the explosive growth in the amount of video data and rapid advance in computing power, extensive research efforts have been devoted to... Sample PDF
Content-Based Video Semantic Analysis
$37.50
Chapter 10
Hossam A. Gabbar, Naila Mahmut
Semantic mining is an essential part in knowledgebase and decision support systems where it enables the extraction of useful knowledge form... Sample PDF
Applications of Semantic Mining on Biological Process Engineering
$37.50
Chapter 11
Gerald Schaefer, Simon Ruszala
Efficient and effective techniques for managing and browsing large image databases are increasingly sought after. This chapter presents a simple yet... Sample PDF
Intuitive Image Database Navigation by Hue-Sphere Browsing
$37.50
Chapter 12
Rong Yan, Apostol Natsev, Murray Campbell
Although important in practice, manual image annotation and retrieval has rarely been studied by means of formal modeling methods. In this chapter... Sample PDF
Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval
$37.50
Chapter 13
Meng Wang, Xian-Sheng Hua, Jinhui Tang, Guo-Jun Qi
This chapter introduces the application of active learning in video annotation. The insufficiency of training data is a major obstacle in... Sample PDF
Active Video Annotation: To Minimize Human Effort
$37.50
Chapter 14
Xin-Jing Wang, Lei Zhang, Xirong Li, Wei-Ying Ma
Although it has been studied for years by computer vision and machine learning communities, image annotation is still far from practical. In this... Sample PDF
Annotating Images by Mining Image Search
$37.50
Chapter 15
Yonghong Tian, Shuqiang Jiang, Tiejun Huang, Wen Gao
With the rapid growth of image collections, content-based image retrieval (CBIR) has been an active area of research with notable recent progress.... Sample PDF
Semantic Classification and Annotation of Images
$37.50
Chapter 16
Arun Kulkarni, Leonard Brown
With advances in computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are... Sample PDF
Association-Based Image Retrieval
$37.50
Chapter 17
Gerald Schaefer
Image retrieval and image compression have been typically pursued separately. Only little research has been done on a synthesis of the two by... Sample PDF
Compressed-Domain Image Retrieval Based on Colour Visual Patterns
$37.50
Chapter 18
M. Singh, X. Cheng, X. He
Discovery of the multimedia resources on network is the focus of the many researches in post semantic web. The task of resources discovery can be... Sample PDF
Resource Discovery Using Mobile Agents
$37.50
Chapter 19
Multimedia Data Indexing  (pages 449-475)
Zhu Li, Yun Fu, Junsong Yuan, Ying Wu, Aggelos Katsaggelos, Thomas S. Huang
The rapid advances in multimedia capture, storage and communication technologies and capabilities have ushered an era of unprecedented growth of... Sample PDF
Multimedia Data Indexing
$37.50
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Reviews and Testimonials

This publication details how current semantic mining tasks play an important role in may fields including random sampling techniques and support vector machine for human computer interaction, manifold learning and subspace methods for data visualization, discriminant analysis for feature selection, and classification trees for data indexing.

– Dacheng Tao, Hong Kong Polytechnic University, Hong Kong
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Topics Covered

  • Active video annotation
  • Association-based image retrieval
  • Content-based video semantic analysis
  • Face recognition and semantic features
  • Intuitive image database navigation
  • Multimedia data indexing
  • Multimedia information representation
  • Multimedia resource annotation
  • Resource discovery using mobile agents
  • Semantic classification of images
  • Visual Data Mining
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Preface

With the explosive growth of multimedia databases in terms of both size and variety, effective and efficient indexing and searching techniques for large-scale multimedia databases have become an urgent research topic in recent years.

For data organization, the conventional approach is based on key words or text description of a multimedia datum. However, it is tedious to give all data text annotation and it is almost impossible for people to capture as well. Moreover, the text description is also not enough to precisely describe a multimedia datum. For example, it is unrealistic to utilize words to describe a music clip; an image says more than a thousand words; and keywords-based video shot description cannot characterize the contents for a specific user. Therefore, it is important to utilize the content based approaches (CbA) to mine the semantic information of a multimedia datum.

In the last ten years, we have witnessed very significant contributions of CbA in semantics targeting for multimedia data organization. CbA means that the data organization, including retrieval and indexing, utilizes the contents of the data themselves, rather than keywords provided by human. Therefore, the contents of a datum could be obtained from techniques in statistics, computer vision, and signal processing. For example, Markov random fields could be applied for image modeling; spatial-temporal analysis is important for video representation; and the Mel frequency cepstral coefficient has been shown to be the most effective method for audio signal classification.

Apart from the conventional approaches mentioned above, machine learning also plays an indispensable role in current semantic mining tasks, e.g., random sampling techniques and support vector machine for human computer interaction, manifold learning and subspace methods for data visualization, discriminant analysis for feature selection, and classification trees for data indexing.

The goal of this IGI book is to provide an introduction about the most recent research and techniques in multimedia semantic mining for new researchers, so that they can go step by step into this field. As a result, they can follow the right way according to their specific applications. The book is also an important reference for researchers in multimedia, a handbook for research students, and a repository for multimedia technologists.

The major contributions of this book are in three aspects: 1) collecting and seeking the recent and most important research results in semantic mining for multimedia data organization, 2) guiding new researchers a comprehensive review on the state-of-the-art techniques for different tasks for multimedia database management, and 3) providing technologists and programmers important algorithms for multimedia system construction.

This edited book attracted submissions from eight countries including Canada, China, France, Japan, Poland, Singapore, UK, and USA. Among these submissions, 19 have been accepted. We strongly believe that it is now an ideal time to publish this edited book with the 19 selected chapters. The contents of this edited book will provide readers with cutting-edge and topical information for their related research.

Accepted chapters are solicited to address a wide range of topics in semantic mining from multimedia databases and an overview of the included chapters is given below.

This book starts from new multimedia information representations (Video Representation and Processing for Multimedia Data Mining) (Image Features from Morphological Scale-spaces) (Face Recognition and Semantic Features), after which learning in multimedia information organization, an important topic in semantic mining, is studied by four chapters (Shape Matching for Foliage Database Retrieval) (Similarity Learning For Motion Estimation) (Active Learning for Relevance Feedback in Image Retrieval) (Visual Data Mining Based on Partial Similarity Concepts). Thereafter, four schemes are presented for semantic analysis in four chapters (Image/Video Semantic Analysis by Semi-Supervised Learning) (Content-Based Video Semantic Analysis) (Semantic Mining for Green Production Systems) (Intuitive Image Database Navigation by Hue-sphere Browsing). The multimedia resource annotation is also essential for a retrieval system and four chapters provide interesting ideas (Hybrid Tagging and Browsing Approaches for Efficient Manual Image Annotation) (Active Video Annotation: To Minimize Human Effort) (Image Auto-Annotation by Search) (Semantic Classification and Annotation of Images). The last part of this book presents other related topics for semantic mining (Association-Based Image Retrieval) (Compressed-domain Image Retrieval based on Colour Visual Patterns) (Multimedia Resource Discovery using Mobile Agent) (Multimedia Data Indexing).

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Author(s)/Editor(s) Biography

Dacheng Tao received the B.Eng. degree from the University of Science and Technology of China (USTC), the MPhil degree from the Chinese University of Hong Kong (CUHK), and the PhD degree from the University of London (Lon). Currently, he is an assistant professor with the Department of Computing in the Hong Kong Polytechnic University, is a visiting professor in the Xi'Dian University, and holds a visiting position at Birkbeck in Lon. His research interests include artificial intelligence, computer vision, data mining, geoinformatics, machine learning, multimedia, remote sensing, statistics, and visual surveillance. He has published more than 80 scientific articles extensively at IEEE TPAMI, TIP, TKDE, TMM, TCSVT, TSMC, CVPR, ICDM; ACM TKDD, Multimedia, KDD etc. with best paper award and nominations. Previously he gained several Meritorious Awards from the International Interdisciplinary Contest in Modeling, which is the highest level mathematical modeling contest in the world, organized by COMAP. He is an associate editor of Neurocomputing (Elsevier) and the Official Journal of the International Association for Statistical Computing -- Computational Statistics & Data Analysis (Elsevier). He authored/edited five books and seven special issues, including CVIU, PR, PRL, SP, and Neurocomputing. He (co-)chaired special sessions, invited sessions, workshops and conferences. He served for around 60 major international conferences including CVPR, ICCV, ECCV, ICDM, KDD, and Multimedia, and around 20 top international journals including TPAMI, TOIS, TIP, TCSVT, TMM, TIFS, TSMC-B, Computer Vision and Image Understanding (CVIU), and Information Science.

Dong Xu is currently an assistant professor at Nanyang Technological University at Singapore. He received the B.Eng. and PhD degrees from the Electronic Engineering and Information Science Department, University of Science and Technology of China, in 2001 and 2005, respectively. During his PhD study, he worked at Microsoft Research Asia and The Chinese University of Hong Kong for more than two years. He also worked at Columbia University for one year as a postdoctoral research scientist. His research interests include computer vision, pattern recognition, statistical learning and multimedia content analysis. He has published more than 20 papers in top venues including T-PAMI, T-IP, T-CSVT, T-SMC-B and CVPR. He is an associate editor of Neurocomputing (Elsevier). He is the guest editors of three special issues on video and event analysis in IEEE Transactions on Circuits Systems for Video Technology (T-CSVT), Computer Vision and Image Understanding (CVIU) and Pattern Recognition Letters (PRL), and a coauthor of a forthcoming book entitled "Semantic Mining Technologies for Multimedia Databases". He was awarded a Microsoft Fellowship in 2004.

Xuelong Li holds a permanent post at Birkbeck College, University of London and a visiting/guest professorship at Tianjin University and University of Science and Technology of China. His research focuses on cognitive computing, image/video processing, pattern recognition, and multimedia. His research activities are partly sponsored by EPSRC, the British Council, Royal Society, and the Chinese Academy of Sciences. He has over a hundred scientific publications with several Best Paper Awards and finalists. He is an author/editor of four books, an associate editor of IEEE Trans. on Image Processing, IEEE Trans. on Circuits and Systems for Video Technology, IEEE Trans. on Systems, Man and Cybernetics Part B, and IEEE Trans. on Systems, Man and Cybernetics Part C. He is also an associate editor (editorial board member) of ten other international journals and a guest co-editor of eight special issues. He has served as a chair of around twenty conferences and a program committee member for more than eighty conferences. He has been a reviewer for over a hundred journals and conferences, including eleven IEEE transactions. He is a academic committee member of the China Society of Image and Graphics, a senior member of the IEEE, the chair of IEEE Systems, Man and Cybernetics Society Technical Committee on Cognitive Computing, and a member of several other technical committees of IEEE Systems, Man and Cybernetics Society and IEEE Signal Processing Society Technical Committee on Machine Learning for Signal Processing (MLSP). He is a Chapters Coordinator of the IEEE Systems, Man and Cybernetics Society.