Abstract
The 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.
Keywords
- High-dimensional Indexing
- Instance Search
- Query Feature
- Leaf Group
- SIFT Features
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Amsaleg, L., Jónsson, B.Þ., Lejsek, H. (2018). Scalability of the NV-tree: Three Experiments. In: Marchand-Maillet, S., Silva, Y., Chávez, E. (eds) Similarity Search and Applications. SISAP 2018. Lecture Notes in Computer Science(), vol 11223. Springer, Cham. https://doi.org/10.1007/978-3-030-02224-2_5
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DOI: https://doi.org/10.1007/978-3-030-02224-2_5
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