Large-Scale Query-by-Image Video Retrieval Using Bloom Filters
This addresses the challenge of scalable video retrieval for applications like media search, though it is incremental as it builds on existing retrieval methods with a specific optimization.
The paper tackles the problem of large-scale image-to-video retrieval by proposing a Bloom filter-based framework to index video segments efficiently, resulting in a 24% improvement in mean average precision and a 4x speed increase compared to prior state-of-the-art.
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.