Ebook: Exploration of Visual Data
- Tags: Computer Imaging Vision Pattern Recognition and Graphics, Image Processing and Computer Vision, Artificial Intelligence (incl. Robotics), Multimedia Information Systems, Data Structures Cryptology and Information Theory
- Series: The Springer International Series in Video Computing 7
- Year: 2003
- Publisher: Springer US
- Edition: 1
- Language: English
- pdf
Exploration of Visual Data presents latest research efforts in the area of content-based exploration of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines.
The two key issues emphasized are "content-awareness" and "user-in-the-loop". The authors provide a comprehensive review on algorithms for visual feature extraction based on color, texture, shape, and structure, and techniques for incorporating such information to aid browsing, exploration, search, and streaming of image and video data. They also discuss issues related to the mixed use of textual and low-level visual features to facilitate more effective access of multimedia data.
Exploration of Visual Data provides state-of-the-art materials on the topics of content-based description of visual data, content-based low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.
Exploration of Visual Data presents latest research efforts in the area of content-based exploration of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines.
The two key issues emphasized are "content-awareness" and "user-in-the-loop". The authors provide a comprehensive review on algorithms for visual feature extraction based on color, texture, shape, and structure, and techniques for incorporating such information to aid browsing, exploration, search, and streaming of image and video data. They also discuss issues related to the mixed use of textual and low-level visual features to facilitate more effective access of multimedia data.
Exploration of Visual Data provides state-of-the-art materials on the topics of content-based description of visual data, content-based low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.
Exploration of Visual Data presents latest research efforts in the area of content-based exploration of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines.
The two key issues emphasized are "content-awareness" and "user-in-the-loop". The authors provide a comprehensive review on algorithms for visual feature extraction based on color, texture, shape, and structure, and techniques for incorporating such information to aid browsing, exploration, search, and streaming of image and video data. They also discuss issues related to the mixed use of textual and low-level visual features to facilitate more effective access of multimedia data.
Exploration of Visual Data provides state-of-the-art materials on the topics of content-based description of visual data, content-based low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.
Content:
Front Matter....Pages i-xvii
Introduction....Pages 1-4
Overview of Visual Information Representation....Pages 5-13
Edge-Based Structural Features....Pages 15-37
Probabilistic Local Structure Models....Pages 39-52
Constructing Table-of-Content for Videos....Pages 53-73
Nonlinearly Sampled Video Streaming....Pages 75-95
Relevance Feedback for Visual Data Retrieval....Pages 97-148
Toward Unification of Keywords and Low-Level Contents....Pages 149-162
Future Research Directions....Pages 163-165
Back Matter....Pages 167-187
Exploration of Visual Data presents latest research efforts in the area of content-based exploration of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines.
The two key issues emphasized are "content-awareness" and "user-in-the-loop". The authors provide a comprehensive review on algorithms for visual feature extraction based on color, texture, shape, and structure, and techniques for incorporating such information to aid browsing, exploration, search, and streaming of image and video data. They also discuss issues related to the mixed use of textual and low-level visual features to facilitate more effective access of multimedia data.
Exploration of Visual Data provides state-of-the-art materials on the topics of content-based description of visual data, content-based low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.
Content:
Front Matter....Pages i-xvii
Introduction....Pages 1-4
Overview of Visual Information Representation....Pages 5-13
Edge-Based Structural Features....Pages 15-37
Probabilistic Local Structure Models....Pages 39-52
Constructing Table-of-Content for Videos....Pages 53-73
Nonlinearly Sampled Video Streaming....Pages 75-95
Relevance Feedback for Visual Data Retrieval....Pages 97-148
Toward Unification of Keywords and Low-Level Contents....Pages 149-162
Future Research Directions....Pages 163-165
Back Matter....Pages 167-187
....