2021
Journal article  Open Access

The VISIONE video search system: exploiting off-the-shelf text search engines for large-scale video retrieval

Amato G., Bolettieri P., Carrara F., Debole F., Falchi F., Gennaro C., Vadicamo L., Vairo C.

Content-based video retrieval  Surrogate text representation  Known item search  Ad-hoc video search  Multimedia and multimodal retrieval  Multimedia information systems  Information systems applications  Video search  Image search  Users and interactive retrieval  Retrieval models and ranking 

This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, 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 from those we tested.

Source: JOURNAL OF IMAGING 7 (2021). doi:10.3390/jimaging7050076



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BibTeX entry
@article{oai:it.cnr:prodotti:456298,
	title = {The VISIONE video search system: exploiting off-the-shelf text search engines for large-scale video retrieval},
	author = {Amato G. and Bolettieri P. and Carrara F. and Debole F. and Falchi F. and Gennaro C. and Vadicamo L. and Vairo C.},
	doi = {10.3390/jimaging7050076},
	journal = {JOURNAL OF IMAGING},
	volume = {7},
	year = {2021}
}