Generative Adversarial Product Quantisation

Abstract

Product Quantisation (PQ) has been recognised as an effective encoding technique for scalable multimedia content analysis. In this paper, we propose a novel learning framework that enables an end-to-end encoding strategy from raw images to compact PQ codes. The system aims to learn both PQ encoding functions and codewords for content-based image retrieval. In detail, we first design a trainable encoding layer that is pluggable into neural networks, so the codewords can be trained in back-forward propagation. Then we integrate it into a Deep Convolutional Generative Adversarial Network (DC-GAN). In our proposed encoding framework, the raw images are directly encoded by passing through the convolutional and encoding layers, and the generator aims to use the codewords as constrained inputs to generate full image representations that are visually similar to the original images. By taking the advantages of the generative adversarial model, our proposed system can produce high-quality PQ codewords and encoding functions for scalable multimedia retrieval tasks. Experiments show that the proposed architecture GA-PQ outperforms the state-of-the-art encoding techniques on three public image datasets.