Multimedia data mining and knowledge discovery
This tightly-edited collection of papers from leading researchers worldwide provides an overview of the current state-of-the-art in the field of multimedia data mining and knowledge discovery, and discusses the variety of hot topics in multimedia data mining research. Consisting of an introductory s...
| Other Authors: | Petrushin, V. A., Khan, Latifur., SpringerLink (Online service) |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
London :
Springer,
©2007.
London : [2007] |
| Physical Description: |
1 online resource (xxv, 521 pages) : illustrations. |
| Subjects: |
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| 245 | 0 | 0 | |a Multimedia data mining and knowledge discovery / |c Valery A. Petrushin and Latifur Khan, eds. |
| 260 | |a London : |b Springer, |c ©2007. | ||
| 264 | 1 | |a London : |b Springer, |c [2007] | |
| 264 | 4 | |c ©2007. | |
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| 520 | |a This tightly-edited collection of papers from leading researchers worldwide provides an overview of the current state-of-the-art in the field of multimedia data mining and knowledge discovery, and discusses the variety of hot topics in multimedia data mining research. Consisting of an introductory section and four topical parts, the book describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field. Written with graduates in mind, this much needed comprehensive reflection of the current state of multimedia data mining and knowledge discovery will also be a valuable resource for researchers in the field. It features a comprehensive introduction to multimedia data mining and its relevance today and presents a global perspective of the field and its various components. This book provides broad, yet thorough and detailed coverage of the subject and contains numerous chapters reference websites with supplementary materials and demonstrations. It explores multimedia data exploration, multimedia data modeling and evaluation, and visualization, and offers an entire part devoted to applications and case studies. Written with graduate students in mind, this much needed comprehensive survey of the current state of multimedia data mining and knowledge discovery will also serve as a valuable resource for researchers with interests in multimedia data mining, summarization, indexing, and retrieval. | ||
| 505 | 0 | |a Into Multimedia Data Mining and Knowledge Discovery -- Multimedia Data Mining: An Overview -- Multimedia Data Exploration and Visualization -- A New Hierarchical Approach for Image Clustering -- Multiresolution Clustering of Time Series and Application to Images -- Mining Rare and Frequent Events in Multi-camera Surveillance Video -- Density-Based Data Analysis and Similarity Search -- Feature Selection for Classification of Variable Length Multiattribute Motions -- Multimedia Data Indexing and Retrieval -- FAST: Fast and Semantics-Tailored Image Retrieval -- New Image Retrieval Principle: Image Mining and Visual Ontology -- Visual Alphabets: Video Classification by End Users -- Multimedia Data Modeling and Evaluation -- Cognitively Motivated Novelty Detection in Video Data Streams -- Video Event Mining via Multimodal Content Analysis and Classification -- Exploiting Spatial Transformations for Identifying Mappings in Hierarchical Media Data -- A Novel Framework for Semantic Image Classification and Benchmark Via Salient Objects -- Extracting Semantics Through Dynamic Context -- Mining Image Content by Aligning Entropies with an Exemplar -- More Efficient Mining Over Heterogeneous Data Using Neural Expert Networks -- A Data Mining Approach to Expressive Music Performance Modeling -- Applications and Case Studies -- Supporting Virtual Workspace Design Through Media Mining and Reverse Engineering -- A Time-Constrained Sequential Pattern Mining for Extracting Semantic Events in Videos -- Multiple-Sensor People Localization in an Office Environment -- Multimedia Data Mining Framework for Banner Images -- Analyzing User's Behavior on a Video Database -- On SVD-Free Latent Semantic Indexing for Iris Recognition of Large Databases -- Mining Knowledge in Computer Tomography Image Databases. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Multimedia systems. | |
| 650 | 2 | |a Data Mining. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Multimédia. | |
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| 650 | 0 | 7 | |a Data mining. |2 cct. |
| 650 | 7 | |a Informatique. |2 eclas. | |
| 650 | 7 | |a Data mining. |2 fast. | |
| 650 | 7 | |a Multimedia systems. |2 fast. | |
| 650 | 1 | 7 | |a Datamining. |2 gtt. |
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| 700 | 1 | |a Khan, Latifur. | |
| 710 | 2 | |a SpringerLink (Online service) | |
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