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Related Experiment Video

Updated: Oct 22, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval.

Giuseppe Amato1, Paolo Bolettieri1, Fabio Carrara1

  • 1Institute of Information Science and Technologies (ISTI), Italian National Research Council (CNR), Via G. Moruzzi 1, 56124 Pisa, Italy.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

VISIONE enables advanced video search by combining text, object, color, and image similarity queries. This unified system efficiently indexes diverse video information for flexible and comprehensive retrieval.

Keywords:
Ad-hoc video searchcontent-based video retrievalimage searchinformation systems applicationsknown item searchmultimedia and multimodal retrievalmultimedia information systemsretrieval models and rankingsurrogate text representationusers and interactive retrievalvideo search

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Multimedia Systems

Background:

  • Traditional video search often relies on limited metadata or simple keyword matching.
  • Integrating diverse search modalities like visual content, object presence, and spatial relationships remains a challenge.

Purpose of the Study:

  • To introduce VISIONE, a novel video search system.
  • To enable complex video queries by integrating multiple search modalities.
  • To present a flexible and efficient method for indexing and retrieving video information.

Main Methods:

  • Developed a system (VISIONE) that encodes keyframe information (visual features, tags, object/color locations) into a unified textual format.
  • Indexed this textual encoding within a single text retrieval engine.
  • Combined multiple search modalities (text, objects, colors, image similarity) for complex query formulation.

Main Results:

  • Demonstrated the flexibility of merging results from various query components (visual, text, location).
  • Conducted an extensive performance analysis using query logs from the Video Browser Showdown (VBS) 2019 competition.
  • Fine-tuned system parameters and strategies based on retrieval performance analysis.

Conclusions:

  • The unified textual encoding approach offers significant flexibility in video search.
  • The system effectively integrates diverse information for complex query satisfaction.
  • Performance analysis facilitated optimization for improved retrieval accuracy.