Data di Pubblicazione:
2019
Abstract:
This paper presents VISIONE, a tool for large-scale video search. The tool can be used for both known-item and ad-hoc video search tasks since it integrates several content-based analysis and re- trieval modules, including a keyword search, a spatial object-based search, and a visual similarity search. Our implementation is based on state-of- the-art deep learning approaches for the content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine.
Tipologia CRIS:
04.01 Contributo in Atti di convegno
Keywords:
Content-based video retrieval; Video search; Convolutional Neural Networks; Known Item Search
Elenco autori:
Vadicamo, Lucia; Carrara, Fabio; Amato, Giuseppe; Gennaro, Claudio; Debole, Franca; Bolettieri, Paolo; Falchi, Fabrizio; Vairo, CLAUDIO FRANCESCO
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Link al Full Text:
Titolo del libro:
MultiMedia Modeling