Enhanced Multimodal Video Retrieval System: Integrating Query Expansion And Cross-modal Temporal Event Retrieval
2025 · van-Thinh Vo, Minh-Khoi Nguyen, Minh-Huy Tran, et al.
Abstract
Multimedia information retrieval from videos remains a challenging problem. While recent systems have advanced multimodal search through semantic, object, and OCR queries - and can retrieve temporally consecutive scenes - they often rely on a single query modality for an entire sequence, limiting robustness in complex temporal contexts. To overcome this, we propose a cross-modal temporal event retrieval framework that enables different query modalities to describe distinct scenes within a sequence. To determine decision thresholds for scene transition and slide change adaptively, we build Kernel Density Gaussian Mixture Thresholding (KDE-GMM) algorithm, ensuring optimal keyframe selection. These extracted keyframes act as compact, high-quality visual exemplars that retain each segment's semantic essence, improving retrieval precision and efficiency. Additionally, the system incorporates a large language model (LLM) to refine and expand user queries, enhancing overall retrieval performa
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