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.
Title of host publication
Proceedings of the International Conference on Similarity Search and Applications (SISAP)
Stéphane Marchand-Maillet, Yasin N. Silva, Edgar Chávez
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