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Video Search with Context-Aware Ranker and Relevance Feedback

Publikace na Matematicko-fyzikální fakulta |
2022

Tento text není v aktuálním jazyce dostupný. Zobrazuje se verze "en".Abstrakt

Interactive video search systems effectively combine text-image embedding approaches and smart user interfaces allowing various means of browsing in intermediate result sets. In this paper, we combine features from VIRET and SOMHunter systems into a novel approach for segment based interactive video retrieval.

Based on our SOMHunter log analysis and VIRET tool performance in known-item search tasks, we focus on two specific features - a combination of context-aware ranking by text queries and Bayesian-like relevance feedback approach for refining scores using promising candidates.