Can I read Orthonormal Product Quantization Network for Scalable Face Image Retrieval on EtoBox?

Orthonormal Product Quantization Network for Scalable Face Image Retrieval by Zhang, Ming; Zhe, Xuefei; Yan, Hong is a scholarly article available to read on EtoBox.

What is Orthonormal Product Quantization Network for Scalable Face Image Retrieval about?

Existing deep quantization methods provided an efficient solution for large-scale image retrieval. However, the significant intra-class variations like pose, illumination, and expressions in face images, still pose a challenge for face image retrieval. In light of this, face image retrieval requires sufficiently powerful learning metrics, which are absent in current deep quantization works. Moreover, to tackle the growing unseen identities in the query stage, face image retrieval drives more demands regarding model generalization and system scalability than general image retrieval tasks. This paper integrates product quantization with orthonormal constraints into an end-to-end deep learning framework to effectively retrieve face images. Specifically, a novel scheme that uses predefined orthonormal vectors as codewords is proposed to enhance the quantization informativeness and reduce codewords' redundancy. A tailored loss function maximizes discriminability among identities in each quantization subspace for both the quantized and original features. An entropy-based regularization term is imposed to reduce the quantization error. Experiments are conducted on four commonly-used face

Author

Zhang, Ming; Zhe, Xuefei; Yan, Hong

Published

2021

Language

EN