# The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

arXiv: https://arxiv.org/abs/2008.02749 · HTML: https://mlsanity.com/paper/2008.02749/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Towards Efficient and Robust Moment Retrieval System: A Unified Framework for Multi-Granularity Models and Temporal Reranking](https://mlsanity.com/paper/2504.08384/) (2025)
- [Sketches image analysis: Web image search engine usingLSH index and DNN InceptionV3](https://mlsanity.com/paper/2105.01147/) (2021)
- [Beyond Flat Unknown Labels in Open-World Object Detection](https://mlsanity.com/paper/2510.09173/) (2025)

## Abstract (arXiv)

In this paper, we describe in details VISIONE, a video search system that allows users to search for videos using textual keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and satisfy user needs. The peculiarity of our approach is that we encode all the information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) have to be merged. In addition, we report an extensive analysis of the system retrieval performance, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies among the ones that we tested.