Abstract
In this paper, we propose an unsupervised feature
learning method called deep binary descriptor with multiquantization (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