Cross-domain multicue fusion for concept-based video indexing

Area of Science:

• Computer Science
• Artificial Intelligence
• Multimedia

Background:

• Accurate concept-based video indexing is crucial for query-by-concept video retrieval.
• The semantic gap between low-level features and high-level concepts hinders video understanding.
• Existing methods struggle with recognizing concepts and extracting linguistic descriptions from videos.

Purpose of the Study:

• To reduce the semantic gap in video retrieval.
• To explore cues beyond low-level features for concept recognition.
• To combine diverse cues and adapt learned knowledge to new video domains.

Main Methods:

• Proposed a framework that jointly exploits multiple cues across multiple video domains.
• Utilized recursive algorithms to learn inter-concept and inter-shot relationships from annotations.
• Developed a fusion model for simultaneous refinement of concept labels and assigned pseudolabels to unseen shots.

Main Results:

• The framework effectively reduces the semantic gap in video indexing.
• Achieved significant improvements over popular baselines on benchmark datasets.
• Demonstrated the framework's ability to accommodate domain changes through cue integration.

Conclusions:

• The proposed framework significantly enhances concept-based video indexing accuracy.
• Jointly exploiting multiple cues and adapting knowledge across domains improves video retrieval performance.
• The method offers a robust solution for bridging the semantic gap in multimedia analysis.