3 research outputs found
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
Recently, deep hashing with Hamming distance metric has drawn increasing
attention for face image retrieval tasks. However, its counterpart deep
quantization methods, which learn binary code representations with
dictionary-related distance metrics, have seldom been explored for the task.
This paper makes the first attempt to integrate product quantization into an
end-to-end deep learning framework for face image retrieval. Unlike prior deep
quantization methods where the codewords for quantization are learned from
data, we propose a novel scheme using predefined orthonormal vectors as
codewords, which aims to enhance the quantization informativeness and reduce
the codewords' redundancy. To make the most of the discriminative information,
we design a tailored loss function that maximizes the identity discriminability
in each quantization subspace for both the quantized and the original features.
Furthermore, an entropy-based regularization term is imposed to reduce the
quantization error. We conduct experiments on three commonly-used datasets
under the settings of both single-domain and cross-domain retrieval. It shows
that the proposed method outperforms all the compared deep hashing/quantization
methods under both settings with significant superiority. The proposed
codewords scheme consistently improves both regular model performance and model
generalization ability, verifying the importance of codewords' distribution for
the quantization quality. Besides, our model's better generalization ability
than deep hashing models indicates that it is more suitable for scalable face
image retrieval tasks