Large-Scale Query-by-Image Video Retrieval Using Bloom Filters
Abstract
We consider the problem of using image queries to retrieve videos from a database. Our focus is on large-scale applications, where it is infeasible to index each database video frame independently. Our main contribution is a framework based on Bloom filters, which can be used to index long video segments, enabling efficient image-to-video comparisons. Using this framework, we investigate several retrieval architectures, by considering different types of aggregation and different functions to encode visual information -- these play a crucial role in achieving high performance. Extensive experiments show that the proposed technique improves mean average precision by 24% on a public dataset, while being 4X faster, compared to the previous state-of-the-art.
Cite
@article{arxiv.1604.07939,
title = {Large-Scale Query-by-Image Video Retrieval Using Bloom Filters},
author = {Andre Araujo and Jason Chaves and Haricharan Lakshman and Roland Angst and Bernd Girod},
journal= {arXiv preprint arXiv:1604.07939},
year = {2016}
}