An Interactive Video Search Tool: A Case Study Using the V3C1 Dataset

This paper presents a prototype of an interactive video search tool for the preparation of MMM 2021 Video Browser Showdown (VBS). Our tool is tailored to enable searching for the public V3C1 dataset associated with various analysis results including detected objects, speech recognition, and visual features. It supports two types of searches: text-based and visual-based. With a text-based search, the tool enables users for querying videos using their textual descriptions, while with a visual-based search, one provides a video example to search for similar videos. Metadata extracted by recent state-of-the-art computer vision algorithms for object detection and visual features are used for accurate search. For an efficient search, the metadata are managed in two database engines: Whoosh and PostgreSQL. The tool also enables users to refine the search results by providing relevance feedback and customizing the intermediate analysis of the query inputs.