A unified scheme of shot boundary detection and anchor shot detection in news video story parsing
Multimedia Tools and Applications
Automatic indexing of news videos through text classification techniques
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Combining audio-based and video-based shot classification systems for news videos segmentation
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
An improved algorithm for anchor shot detection
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
On-line video abstract generation of multimedia news
Multimedia Tools and Applications
A general Framework of video segmentation to logical unit based on conditional random fields
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Style learning based story boundary detection for Chinese broadcast news videos
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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This research proposes a two-level, multi-modal framework to perform the segmentation and classification of news video into single-story semantic units. The video is analyzed at the shot and story unit (or scene) levels using a variety of features and techniques. At the shot level, we employ Decision Trees technique to classify the shots into one of 13 predefined categories or mid-level features. At the scene/story level, we perform the HMM (Hidden Markov Models) analysis to locate story boundaries. Our initial results indicate that we could achieve a high accuracy of over 95% for shot classification, and over 89% in F1 measure on scene/story boundary detection. Detailed analysis reveals that HMM is effective in identifying dominant features, which helps in locating story boundaries. Our eventual goal is to support the retrieval of news video at story unit level, together with associated texts retrieved from related news sites on the web.