DAHP: Deep Attention-Guided Hashing With Pairwise Labels

Abstract

To address the problem of inadequate feature extraction and binary code discrete optimization faced by deep hashing methods using a relaxation-quantization strategy, a novel deep attention-guided hashing method with pairwise labels (DAHP) is proposed to enhance global feature fusion, better learn the contextual information of image features to effectively enhance the feature representation, and solve the problem of losing feature information in discrete optimization by optimizing the loss function. First, we introduce a new concept called the anchor hash code generation(AHCG) algorithm, we train the ResNet with the position attention and channel attention mechanisms with the anchor points in Hamming space as supervised information, we fit the binary code representing the picture to the vicinity of each anchor point, and finally, we use the optimized loss function to calculate the pairwise loss and the anchor loss, allowing the hash function to generate hash code with strong discriminative power. The experiments were conducted on four benchmark datasets, and the retrieval accuracy of the proposed method outperformed the retrieval accuracies of the state-of-the-art methods.