Video handling based on structured information for hypermedia systems
International conference on Multimedia information systems '91
Automatic partitioning of full-motion video
Multimedia Systems
Automatic text recognition for video indexing
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Maintaining knowledge about temporal intervals
Communications of the ACM
Cinematic Primitives for Multimedia
IEEE Computer Graphics and Applications
The Stratification System - A Design Emvironment for Random Access
Proceedings of the Third International Workshop on Network and Operating System Support for Digital Audio and Video
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
A Rule-Based Scheme to Make Personal Digests from Video Program Meta Data
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Significant Scene Extraction Method Using Situation Importance
ICDCSW '04 Proceedings of the 24th International Conference on Distributed Computing Systems Workshops - W7: EC (ICDCSW'04) - Volume 7
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Video indexing based on contents annotations can fully explore semantic information of video data. However, the most difficult and time-consuming process in annotation-based indexing is to identify appropriate video intervals for various semantic contents manually. Thus, automatic discovering video intervals from video data will be helpful for the indexing work. For this purpose, we propose "semantic structures" of video data and a mechanism for discovering semantic structures. The basic concept of our approach is to (1) discover consecutive sequences of shots from video data, each of which represents a consistent action or situation, and (2) index each of the discovered video intervals based on its semantics. A semantic structure is a collection of discovered video intervals that are classified into three categories: "unchanged" (i.e. actors or backgrounds are unchanged throughout the interval), "gradually changing" (i.e. actors or backgrounds are changing shot by shot) and "multiplexing" (i.e. individual actors or backgrounds are appearing by turns). The mechanism discovers these types of video intervals by comparing and contrasting similarity between each shot, and indexes each of discovered intervals by using indexing algorithms prepared for each type. We show how well our approach works for identifying video intervals with some experimental results.