Multimedia Data Mining and Knowledge Discovery

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.

• Publication year

  2006

• Venue

  J. Electronic Imaging

• Publication date

  2006-12-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1117/1.3040688
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

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