Attention-aware invertible hashing network with skip connections

Abstract

Abstract In recent years, Convolutional Neural Networks (CNNs) have shown promising performance on image hashing retrieval. However, due to the information-discarded nature of CNN, some meaningful information can not be further extracted into a deep level and embedded into hash codes. To solve the problem, this study attempts to design an invertible CNN feature extractor to fully maintain input information meanwhile having well generalization ability. Specifically, we propose a novel Attention-Aware Invertible Hashing Network with Skip Connection (AIHN-SC) for image retrieval. Represented by an invertible feature, the hash code can be learned and generated from image characteristics preserving all input information. For achieving favourable generalization ability in our invertible architecture, we present a novel spatial attention mechanism to highlight regions involving semantic information. In addition, we introduce two kinds of skip connection, i.e. hierarchical and residual connections, which aim to provide richer knowledges for hash code learning and ease our training process. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed AIHN-SC and show the significant performance in image retrieval against the state-of-the-arts.