Product Quantization Network for Fast Visual Search

Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features. By constructing the approximation function, we extend the hard-assignment quantization to soft-assignment quantization. Thanks to the differentiable property of the soft-assignment quantization, the product quantization operation can be integrated as a layer in a convolutional neural network, constructing the proposed product quantization network (PQN). Meanwhile, by extending the triplet loss to the asymmetric triplet loss, we directly optimize the retrieval accuracy of the learned representation based on asymmetric similarity measurement. Utilizing PQN, we can learn a discriminative and compact image representation in an end-to-end manner, which further enables a fast and accurate image retrieval. By revisiting residual quantization, we further extend the proposed PQN to residual product quantization network (RPQN). Benefited from the residual learning triggered by residual quantization, RPQN achieves a higher accuracy than PQN using the same computation cost. Moreover, we extend PQN to temporal product quantization network (TPQN) by exploiting temporal consistency in videos to speed up the video retrieval. It integrates frame-wise feature learning, frame-wise features aggregation and video-level feature quantization in a single neural network. Comprehensive experiments conducted on multiple public benchmark datasets demonstrate the state-of-the-art performance of the proposed PQN, RPQN and TPQN in fast image and video retrieval.