TY - GEN
T1 - Product Quantization Network for Fast Image Retrieval
AU - Yu, Tan
AU - Yuan, Junsong
AU - Fang, Chen
AU - Jin, Hailin
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features. By extending the hard assignment to soft assignment, we make it feasible to incorporate the product quantization as a layer of a convolutional neural network and propose our product quantization network. Meanwhile, we come up with a novel asymmetric triplet loss, which effectively boosts the retrieval accuracy of the proposed product quantization network based on asymmetric similarity. Through the proposed product quantization network, we can obtain a discriminative and compact image representation in an end-to-end manner, which further enables a fast and accurate image retrieval. Comprehensive experiments conducted on public benchmark datasets demonstrate the state-of-the-art performance of the proposed product quantization network.
AB - Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features. By extending the hard assignment to soft assignment, we make it feasible to incorporate the product quantization as a layer of a convolutional neural network and propose our product quantization network. Meanwhile, we come up with a novel asymmetric triplet loss, which effectively boosts the retrieval accuracy of the proposed product quantization network based on asymmetric similarity. Through the proposed product quantization network, we can obtain a discriminative and compact image representation in an end-to-end manner, which further enables a fast and accurate image retrieval. Comprehensive experiments conducted on public benchmark datasets demonstrate the state-of-the-art performance of the proposed product quantization network.
UR - https://www.scopus.com/pages/publications/85055096929
U2 - 10.1007/978-3-030-01246-5_12
DO - 10.1007/978-3-030-01246-5_12
M3 - Conference contribution
AN - SCOPUS:85055096929
SN - 9783030012458
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 191
EP - 206
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Hebert, Martial
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -