NDLI: Semantic multi-modal analysis, structuring, and visualization for candid personal interaction videos.

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Semantic multi-modal analysis, structuring, and visualization for candid personal interaction videos.

Content Provider	CiteSeerX
Author	Haubold, Alexander
Abstract	Videos are rich in multimedia content and semantics, which should be used by video browsers to better present the audio-visual information to the viewer. Ubiquitous video players allow for content to be scanned linearly, rarely providing summaries or methods for searching. Through analysis of audio and video tracks, it is possible to extract text transcripts from audio, displayed text from video, and higher-level semantics through speaker identification and scene analysis. External data sources, when available, can be used to cross-reference the video content and impose a structure for organization. Various research tools have addressed video summarization and browsing using one or more of these modalities; however, most of them assume edited videos as input. We focus our research on genres in personal interaction videos and collections of such videos in their unedited form. We present and verify formal models for their structure, and develop methods for their automatic analysis, summarization and indexing. We specify the characteristic semantic components of three related genres of candidly captured videos: formal instructions or lectures, student team project presentations, and discussions. For each genre, we design and
File Format	PDF
Access Restriction	Open
Subject Keyword	Higher-level Semantics Ubiquitous Video Player Various Research Tool Audio-visual Information Personal Interaction Video Semantic Multi-modal Analysis Formal Instruction Related Genre Unedited Form External Data Source Edited Video Student Team Project Presentation Video Content Video Summarization Formal Model Multimedia Content Candid Personal Interaction Video Video Track Scene Analysis Characteristic Semantic Component Automatic Analysis Video Browser Speaker Identification Text Transcript
Content Type	Text

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