Learning Deep Binary Descriptor With Multi-Quantization

Yueqi Duan, Jiwen Lu, Ziwei Wang, Jianjiang Feng, Jie Zhou; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1183-1192

Abstract


In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching. Existing learning-based binary descriptors such as compact binary face descriptor (CBFD) and DeepBit utilize the rigid sign function for binarization despite of data distributions, thereby suffering from severe quantization loss. In order to address the limitation, our DBD-MQ considers the binarization as a multi-quantization task. Specifically, we apply a K-AutoEncoders (KAEs) network to jointly learn the parameters and the binarization functions under a deep learning framework, so that discriminative binary descriptors can be obtained with a fine-grained multi-quantization. Extensive experimental results on different visual analysis including patch retrieval, image matching and image retrieval show that our DBD-MQ outperforms most existing binary feature descriptors.

Related Material


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[bibtex]
@InProceedings{Duan_2017_CVPR,
author = {Duan, Yueqi and Lu, Jiwen and Wang, Ziwei and Feng, Jianjiang and Zhou, Jie},
title = {Learning Deep Binary Descriptor With Multi-Quantization},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}