Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills
  1. Outputs

VISIONE at VBS2019

Conference Paper
Publication Date:
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.
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
Content-based video retrieval; Video search; Convolutional Neural Networks; Known Item Search
List of contributors:
Vadicamo, Lucia; Carrara, Fabio; Amato, Giuseppe; Gennaro, Claudio; Debole, Franca; Bolettieri, Paolo; Falchi, Fabrizio; Vairo, CLAUDIO FRANCESCO
Authors of the University:
AMATO GIUSEPPE
BOLETTIERI PAOLO
CARRARA FABIO
DEBOLE FRANCA
FALCHI FABRIZIO
GENNARO CLAUDIO
VADICAMO LUCIA
VAIRO CLAUDIO FRANCESCO
Handle:
https://iris.cnr.it/handle/20.500.14243/388376
Full Text:
https://iris.cnr.it//retrieve/handle/20.500.14243/388376/191541/prod_403935-doc_140675.pdf
Book title:
MultiMedia Modeling
  • Overview

Overview

URL

https://link.springer.com/chapter/10.1007%2F978-3-030-05716-9_51
  • Use of cookies

Powered by VIVO | Designed by Cineca | 26.5.0.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)