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Adaptive Multimedia Retrieval: User, Context, and Feedback [electronic resource] : 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers / edited by Stéphane Marchand-Maillet, Eric Bruno, Andreas Nürnberger, Marcin Detyniecki.

By: Contributor(s): Material type: TextTextSeries: Lecture Notes in Computer Science ; 4398Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007Description: XII, 276 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540715450
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 025.04 23
LOC classification:
  • QA75.5-76.95
Online resources:
Contents:
Ontology-Based Retrieval and Annotation -- A Method for Processing the Natural Language Query in Ontology-Based Image Retrieval System -- SAFIRE: Towards Standardized Semantic Rich Image Annotation -- Ontology-Supported Video Modeling and Retrieval -- Ranking and Similarity Measurements -- Learning to Retrieve Images from Text Queries with a Discriminative Model -- A General Principled Method for Image Similarity Validation -- Rank-Test Similarity Measure Between Video Segments for Local Descriptors -- Music Information Retrieval -- Can Humans Benefit from Music Information Retrieval? -- Visual Modelling -- A New Approach to Probabilistic Image Modeling with Multidimensional Hidden Markov Models -- 3D Face Recognition by Modeling the Arrangement of Concave and Convex Regions -- Fuzzy Semantic Action and Color Characterization of Animation Movies in the Video Indexing Task Context -- Retrieval of Document Images Based on Page Layout Similarity -- Adaptive Retrieval -- Multimedia Content Adaptation Within the CAIN Framework Via Constraints Satisfaction and Optimization -- Aspects of Adaptivity in P2P Information Retrieval -- Interactive Museum Guide: Accurate Retrieval of Object Descriptions -- Structuring Multimedia -- Semantic Image Retrieval Using Region-Based Relevance Feedback -- Image Retrieval with Segmentation-Based Query -- Fast Structuring of Large Television Streams Using Program Guides -- User Integration and Profiling -- Variation of Relevance Assessments for Medical Image Retrieval -- An Efficient Collaborative Information Retrieval System by Incorporating the User Profile -- The Potential of User Feedback Through the Iterative Refining of Queries in an Image Retrieval System.
In: Springer eBooks
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Ontology-Based Retrieval and Annotation -- A Method for Processing the Natural Language Query in Ontology-Based Image Retrieval System -- SAFIRE: Towards Standardized Semantic Rich Image Annotation -- Ontology-Supported Video Modeling and Retrieval -- Ranking and Similarity Measurements -- Learning to Retrieve Images from Text Queries with a Discriminative Model -- A General Principled Method for Image Similarity Validation -- Rank-Test Similarity Measure Between Video Segments for Local Descriptors -- Music Information Retrieval -- Can Humans Benefit from Music Information Retrieval? -- Visual Modelling -- A New Approach to Probabilistic Image Modeling with Multidimensional Hidden Markov Models -- 3D Face Recognition by Modeling the Arrangement of Concave and Convex Regions -- Fuzzy Semantic Action and Color Characterization of Animation Movies in the Video Indexing Task Context -- Retrieval of Document Images Based on Page Layout Similarity -- Adaptive Retrieval -- Multimedia Content Adaptation Within the CAIN Framework Via Constraints Satisfaction and Optimization -- Aspects of Adaptivity in P2P Information Retrieval -- Interactive Museum Guide: Accurate Retrieval of Object Descriptions -- Structuring Multimedia -- Semantic Image Retrieval Using Region-Based Relevance Feedback -- Image Retrieval with Segmentation-Based Query -- Fast Structuring of Large Television Streams Using Program Guides -- User Integration and Profiling -- Variation of Relevance Assessments for Medical Image Retrieval -- An Efficient Collaborative Information Retrieval System by Incorporating the User Profile -- The Potential of User Feedback Through the Iterative Refining of Queries in an Image Retrieval System.

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