Other formats:
BibTeX
LaTeX
RIS
@inproceedings{1341689,
author = {Novák, David and Čech, Jan and Zezula, Pavel},
address = {New York},
booktitle = {Similarity Search and Applications: 8th International Conference, SISAP 2015, Glasgow, UK, October 12-14, 2015, Proceedings},
doi = {http://dx.doi.org/10.1007/978-3-319-25087-8_22},
keywords = {metric indexing; deep convolutional neural network; contentbased image retrieval},
howpublished = {tištěná verze "print"},
language = {eng},
location = {New York},
isbn = {978-3-319-25086-1},
pages = {237-243},
publisher = {Springer International Publishing},
title = {Efficient Image Search with Neural Net Features},
url = {http://dx.doi.org/10.1007/978-3-319-25087-8_22},
year = {2015}
}
TY - JOUR
ID - 1341689
AU - Novák, David - Čech, Jan - Zezula, Pavel
PY - 2015
TI - Efficient Image Search with Neural Net Features
PB - Springer International Publishing
CY - New York
SN - 9783319250861
KW - metric indexing
KW - deep convolutional neural network
KW - contentbased image retrieval
UR - http://dx.doi.org/10.1007/978-3-319-25087-8_22
L2 - http://dx.doi.org/10.1007/978-3-319-25087-8_22
N2 - We present an efficiency evaluation of similarity search techniques applied on visual features from deep neural networks. Our test collection consists of 20 million 4096-dimensional descriptors (320GB of data). We test approximate k-NN search using several techniques, specifically FLANN library (a popular in-memory implementation of k-d tree forest), M-Index (that uses recursive Voronoi partitioning of a metric space), and PPP-Codes, which work with memory codes of metric objects and use disk storage for candidate refinement. Our evaluation shows that as long as the data fit in main memory, the FLANN and the M-Index have practically the same ratio between precision and response time. The PPP-Codes identify candidate sets ten times smaller then the other techniques and the response times are around 500 ms for the whole 20M dataset stored on the disk. The visual search with this index is available as an online demo application. The collection of 20M descriptors is provided as a public dataset to academic community.
ER -
NOVÁK, David, Jan ČECH and Pavel ZEZULA. Efficient Image Search with Neural Net Features. Online. In \textit{Similarity Search and Applications: 8th International Conference, SISAP 2015, Glasgow, UK, October 12-14, 2015, Proceedings}. New York: Springer International Publishing, 2015. p.~237-243. ISBN~978-3-319-25086-1. Available from: https://dx.doi.org/10.1007/978-3-319-25087-8\_{}22. [citováno 2024-04-24]
|
