36 research outputs found
ViRMA: Virtual Reality Multimedia Analytics at LSC 2021
In this paper we describe the first iteration of the ViRMA prototype system, a novel approach to multimedia analysis in virtual reality and inspired by the M3 data model. We intend to evaluate our approach via the Lifelog Search Challenge (LSC) to serve as a benchmark against other multimedia analytics systems
Scalability of the NV-tree: Three Experiments
International audienceThe NV-tree is a scalable approximate high-dimensional indexing method specifically designed for large-scale visual instance search. In this paper, we report on three experiments designed to evaluate the performance of the NV-tree. Two of these experiments embed standard benchmarks within collections of up to 28.5 billion features, representing the largest single-server collection ever reported in the literature. The results show that indeed the NV-tree performs very well for visual instance search applications over large-scale collections
Dynamicity and Durability in Scalable Visual Instance Search.
Visual instance search involves retrieving from a collection of images the
ones that contain an instance of a visual query. Systems designed for visual
instance search face the major challenge of scalability: a collection of a few
million images used for instance search typically creates a few billion
features that must be indexed. Furthermore, as real image collections grow
rapidly, systems must also provide dynamicity, i.e., be able to handle on-line
insertions while concurrently serving retrieval operations. Durability, which
is the ability to recover correctly from software and hardware crashes, is the
natural complement of dynamicity. Durability, however, has rarely been
integrated within scalable and dynamic high-dimensional indexing solutions.
This article addresses the issue of dynamicity and durability for scalable
indexing of very large and rapidly growing collections of local features for
instance retrieval. By extending the NV-tree, a scalable disk-based
high-dimensional index, we show how to implement the ACID properties of
transactions which ensure both dynamicity and durability. We present a detailed
performance evaluation of the transactional NV-tree: (i) We show that the
insertion throughput is excellent despite the overhead for enforcing the ACID
properties; (ii) We also show that this transactional index is truly scalable
using a standard image benchmark embedded in collections of up to 28.5 billion
high-dimensional vectors; the largest single-server evaluations reported in the
literature