Product Quantization Network for Fast Image Retrieval

Tan Yu, Junsong Yuan, Chen Fang, Hailin Jin ; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 186-201

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Yu_2018_ECCV,
author = {Yu, Tan and Yuan, Junsong and Fang, Chen and Jin, Hailin},
title = {Product Quantization Network for Fast Image Retrieval},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}