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Overview
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low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.
Synopsis
This work provides detailed descriptions of recent advances in the area of image and video data exploration, for researchers, practitioners, and graduate students in the areas of multimedia information systems, multimedia databases, computer vision, and machine learning. New methods and algorithms are presented for learning from real-time user interactions, visual object/pattern extraction and representation, temporal video segmentation, content- aware low-bit-rate video streaming, and combined learning in a joint textual and visual domain. The author works in the private sector. Annotation ©2003 Book News, Inc., Portland, OR