Object-Based Access to TV Rushes Video

Recent years have seen the development of different modalities for video retrieval. The most common of these are (1) to use text from speech recognition or closed captions, (2) to match keyframes using image retrieval techniques like colour and texture [6] and (3) to use semantic features like “indo...

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Bibliographic Details
Published inAdvances in Information Retrieval pp. 476 - 479
Main Authors Smeaton, Alan F., Jones, Gareth J. F., Lee, Hyowon, O’Connor, Noel E., Sav, Sorin
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Computer science; control theory; systems
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Query Image
Relevance Feedback
Segmented Object
Software
Video Object
Video Retrieval
Information retrieval
Information system
Television
Object oriented
Video signal
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Summary:Recent years have seen the development of different modalities for video retrieval. The most common of these are (1) to use text from speech recognition or closed captions, (2) to match keyframes using image retrieval techniques like colour and texture [6] and (3) to use semantic features like “indoor”, “outdoor” or “persons”. Of these, text-based retrieval is the most mature and useful, while image-based retrieval using low-level image features usually depends on matching keyframes rather than whole-shots. Automatic detection of video concepts is receiving much attention and as progress is made in this area we will see consequent impact on the quality of video retrieval. In practice it is the combination of these techniques which realises the most useful, and effective, video retrieval as shown by us repeatedly in TRECVid [5].
ISBN:9783540333470
3540333479
ISSN:0302-9743
1611-3349
DOI:10.1007/11735106_45