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More About This Textbook
Overview
In this book, Wensheng Zhou and C.-C. Jay Kuo survey the latest advances in efficient, automated multimedia content analysis, indexing, and access for the next-generation multimedia Web. They present powerful new algorithms for analyzing the perceptual and conceptual contents of any multimedia stream, and introduce intelligent software agents for processing and decomposing multimedia streams in real time. Finally, they demonstrate how to integrate automated video analysis, clustering, and classification to organize, present, and customize immense amounts of content with unprecedented performance. Coverage includes:
Whether you're an engineer, systems designer, or researcher, Intelligent Systems for Video Analysis and Access Over the Internet brings together breakthroughs you can use to enhance virtually any networked multimedia application.
IMSC Press Multimedia Series / Andrew Tescher, Series Editor
Part of the new IMSC Press Series from the Integrated Multimedia System Center at the University of Southern California, a federally funded center specializing in cutting-edge multimedia research.
Editorial Reviews
From The Critics
The latest advances in automated multimedia content analysis, indexing, and access for the next-generation multimedia Web are surveyed, and new algorithms are presented for analyzing the perceptual and conceptual contents of any multimedia stream. Intelligent software agents are introduced for processing and decomposing multimedia streams in real time, and integration of automated video analysis, clustering, and classification is demonstrated. Chapters cover on-line scene change detection of multicast video, knowledge-based video hierarchical classification, and video/audio/text feature representation and analysis. Zhou works in an information science lab. Kuo teaches electrical engineering and mathematics at the University of Southern California. Annotation c. Book News, Inc., Portland, ORProduct Details
Related Subjects
Table of Contents
Preface
PREFACE
The explosion of on-line web information has given rise to many query-based textsearch engines (such as Alta Vista) and manually constructed topic hierarchies (suchas Yahoo!). With the current growth rate of web information, especially broadbandmultimedia data, query data are growing incomprehensibly large and manual classification in topic hierarchies is creating a major bottleneck. Consequently, the hugeamount of multimedia data is imposing on people a heavy burden of manipulating, searching, interpreting, skimming, and integrating information. Thus, efficientmultimedia content analysis tools are needed to address these user's needs.
This book presents a solution to problems arising from the demand for fastinformation access and for sharing in real-time multimedia transmission over theInternet. We present in this book a solution which exploits software agents thatare placed throughout the network environment. These hierarchical video analysisagents process multimedia streams in real time, and automatically decompose andunderstand the multimedia content so as to facilitate information access and sharing.
Multimedia content contains both the perceptual content such as color, motion,or acoustic features and the conceptual content, which is specified based on conceptsor semantics that can be expressed by text descriptions. Both types of contents areembedded simultaneously in multimedia streams, and usually are complementaryto each other. This book adaptively analyzes both kinds of video contents bycombining mixed media cues from audio, video and text.
First, a high-performance module for on-line video segmentation based on scene-change detection isdescribed. The module serves as the first step of any videostream construction and analysis. To meet the high computational demand, ourproposed video scene change detection algorithms are very efficient while maintaining high accuracy and recall rates for fast on-line video analysis.
Second, the perceptual features of audio and video data are analyzed in abottom-up manner and integrated so as to discriminate among the different eventsin any video stream effectively. An efficient decision-tree learning algorithm is usedto induce a set of if-then rules which link perceptual features with the video conceptual semantic contents. These rules not only serve as a video classifier, but alsoguide on-line real-time video/audio feature extraction and data redistribution. Anovel knowledge-based system, where knowledge is stored as learned rules, is proposed and described in this book to serve as a video semantic inference/classificationengine.
Third, we present our proposed hierarchical video categorization scheme basedon machine learning of the text information contained in a video—a scheme whichprovides a good complement to the video/audio classification subsystem. Thelearned text features for each video category are also stored in the knowledge base.To fuse the text classifier and the audio/video classifier, a media cue optimizer thatis trained by using the cue probability distribution based on the concept hierarchyis adopted to guide real-time media query and analysis.
The integration of hierarchical video analysis, clustering and classification allows a large amount of multimedia data to be organized and presented to usersin an individualized and comprehensible way. A general hierarchical concept treescheme is used to organize a video into a table-of-contents for video applications andenables a comprehensive agent-based solution to real-time multimedia distributionand sharing over the Internet.
Introduction
PREFACE
The explosion of on-line web information has given rise to many query-based textsearch engines (such as Alta Vista) and manually constructed topic hierarchies (suchas Yahoo!). With the current growth rate of web information, especially broadbandmultimedia data, query data are growing incomprehensibly large and manual classification in topic hierarchies is creating a major bottleneck. Consequently, the hugeamount of multimedia data is imposing on people a heavy burden of manipulating, searching, interpreting, skimming, and integrating information. Thus, efficientmultimedia content analysis tools are needed to address these user's needs.
This book presents a solution to problems arising from the demand for fastinformation access and for sharing in real-time multimedia transmission over theInternet. We present in this book a solution which exploits software agents thatare placed throughout the network environment. These hierarchical video analysisagents process multimedia streams in real time, and automatically decompose andunderstand the multimedia content so as to facilitate information access and sharing.
Multimedia content contains both the perceptual content such as color, motion,or acoustic features and the conceptual content, which is specified based on conceptsor semantics that can be expressed by text descriptions. Both types of contents areembedded simultaneously in multimedia streams, and usually are complementaryto each other. This book adaptively analyzes both kinds of video contents bycombining mixed media cues from audio, video and text.
First, a high-performance module for on-line video segmentation based on scene-changedetection is described. The module serves as the first step of any videostream construction and analysis. To meet the high computational demand, ourproposed video scene change detection algorithms are very efficient while maintaining high accuracy and recall rates for fast on-line video analysis.
Second, the perceptual features of audio and video data are analyzed in abottom-up manner and integrated so as to discriminate among the different eventsin any video stream effectively. An efficient decision-tree learning algorithm is usedto induce a set of if-then rules which link perceptual features with the video conceptual semantic contents. These rules not only serve as a video classifier, but alsoguide on-line real-time video/audio feature extraction and data redistribution. Anovel knowledge-based system, where knowledge is stored as learned rules, is proposed and described in this book to serve as a video semantic inference/classificationengine.
Third, we present our proposed hierarchical video categorization scheme basedon machine learning of the text information contained in a video—a scheme whichprovides a good complement to the video/audio classification subsystem. Thelearned text features for each video category are also stored in the knowledge base.To fuse the text classifier and the audio/video classifier, a media cue optimizer thatis trained by using the cue probability distribution based on the concept hierarchyis adopted to guide real-time media query and analysis.
The integration of hierarchical video analysis, clustering and classification allows a large amount of multimedia data to be organized and presented to usersin an individualized and comprehensible way. A general hierarchical concept treescheme is used to organize a video into a table-of-contents for video applications andenables a comprehensive agent-based solution to real-time multimedia distributionand sharing over the Internet.