Towards An All-purpose Content-based Multimedia Information Retrieval System
2019 Β· Ralph Gasser, Luca Rossetto, Heiko Schuldt
Abstract
The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from an
Authors
(none)
Tags
Stats
Related papers
- A New Benchmark And Approach For Fine-grained Cross-media Retrieval (2019)17.33
- A Multimodal Deep Learning Framework For Scalable Content Based Visual Media Retrieval (2021)0.00
- Clamr: Contextualized Late-interaction For Multimodal Content Retrieval (2025)0.00
- Enhanced Multimodal Video Retrieval System: Integrating Query Expansion And Cross-modal Temporal Event Retrieval (2025)0.00
- Verve: Versatile Retrieval For Videos Via Unified Embeddings (2026)0.00
- Composed Multi-modal Retrieval: A Survey Of Approaches And Applications (2025)3.88
- IDMR: Towards Instance-driven Precise Visual Correspondence In Multimodal Retrieval (2025)2.29
- Query By Semantic Sketch (2019)0.00