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Video Search with Collage Queries

Publication at Faculty of Mathematics and Physics, Central Library of Charles University |
2021

Abstract

Nowadays, popular web search portals enable users to find available images corresponding to a provided free-form text description. With such sources of example images, a suitable composition/collage of images can be constructed as an appropriate visual query input to a known-item search system.

In this paper, we investigate a querying approach enabling users to search videos with a multi-query consisting of positioned example images, so-called collage query, depicting expected objects in a searched scene. The approach relies on images from external search engines, partitioning of preselected representative video frames, relevance scoring based on deep features extracted from images/frames, and is currently integrated into the open-source version of the SOMHunter system providing additional browsing capabilities.