Abstract
In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval. Most deep hashing approaches use the high layer to extract the powerful semantic representations. However, these methods have limited ability for fine-grained image retrieval because the semantic features extracted from the high layer are difficult in capturing the subtle differences. To this end, we propose a novel two-pyramid hashing architecture to learn both the semantic information and the subtle appearance details for fine-grained image search. Inspired by the feature pyramids of convolutional neural network, a vertical pyramid is proposed to capture the high-layer features and a horizontal pyramid combines multiple low-layer features with structural information to capture the subtle differences. To fuse the low-level features, a novel combination strategy, called consensus fusion, is proposed to capture all subtle information from several low-layers for finer retrieval. Extensive evaluation on two fine-grained datasets CUB-200-2011 and Stanford Dogs demonstrate that the proposed method achieves significant performance compared with the state-of-art baselines.
Abstract (translated)
近年来，基于深度网络的哈希算法已成为大规模图像检索的一种主要方法。大多数深度散列方法使用高层来提取强大的语义表示。然而，这些方法对于细粒度图像检索的能力有限，因为从高层提取的语义特征难以捕捉细微的差异。为此，我们提出了一种新的双金字塔散列结构来学习语义信息和精细图像搜索的细微外观细节。在卷积神经网络特征金字塔的启发下，提出了一种垂直金字塔来捕捉高层次特征，水平金字塔将多个低层次特征与结构信息相结合来捕捉细微差异。为了融合低层次特征，提出了一种新的组合策略，称为共识融合，从多个低层次捕获所有细微信息，以实现更精细的检索。对两个细粒度数据集CUB-200-2011和斯坦福犬进行了广泛的评估，结果表明，与最先进的基线相比，该方法取得了显著的性能。
URL
https://arxiv.org/abs/1904.02325
