Video Search with Context-Aware Ranker and Relevance Feedback
2022; Springer Science+Business Media; Linguagem: Inglês
10.1007/978-3-030-98355-0_46
ISSN1611-3349
AutoresJakub Lokoč, František Mejzlík, Tomáš Soućek, Patrik Dokoupil, Ladislav Peška,
Tópico(s)Video Analysis and Summarization
ResumoInteractive 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.
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