Exploration of Visual Data

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low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.

• Boosting
• Computer Vision
• Image segmentation
• Information
• Layout
• Multimedia
• Textur
• algorithms
• classification
• information system
• learning
• machine learning
• modeling

• Siemens Corporation, Princeton, USA

  Xiang Sean Zhou

• Microsoft Research, Redmond, USA

  Yong Rui

• University of Illinois at Urbana-Champaign, Urbana, USA

  Thomas S. Huang

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