To Aggregate or Not to Aggregate: Selective Match Kernels for Image Search
Abstract
This paper considers a family of metrics to compare images based on their local descriptors. It encompasses the VLAD descriptor and matching techniques such as Hamming Embedding. Making the bridge between these approaches leads us to propose a match kernel that takes the best of existing techniques by combining an aggregation procedure with a selective match kernel. Finally, the representation underpinning this kernel is approximated, providing a large scale image search both precise and scalable, as shown by our experiments on several benchmarks.
Cite
Text
Tolias et al. "To Aggregate or Not to Aggregate: Selective Match Kernels for Image Search." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.177Markdown
[Tolias et al. "To Aggregate or Not to Aggregate: Selective Match Kernels for Image Search." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/tolias2013iccv-aggregate/) doi:10.1109/ICCV.2013.177BibTeX
@inproceedings{tolias2013iccv-aggregate,
title = {{To Aggregate or Not to Aggregate: Selective Match Kernels for Image Search}},
author = {Tolias, Giorgos and Avrithis, Yannis and Jegou, Herve},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.177},
url = {https://mlanthology.org/iccv/2013/tolias2013iccv-aggregate/}
}