The low storage and strong representation capabilities of hash codes for image retrievalhas made hashing technologies very popular. Several existing deep hashing methods focuson the task of general image retrieval, while neglecting the task of fine-grained image retrieval. Recently, some fine-grained hashing methods have been proposed to capture the subtle differences, which mainly utilize the single-modality visual features to solve the discriminative region localization while ignoring the semantic information. In this letter, we propose a correlation filtering hashing (CFH) method to learn discrete binary codes, which can adequately take advantage of the cross-modal correlation between the semantic information and the visual features for discriminative region localization. Specifically, we utilize a feature pyramid network to learn multi-level visual features. Subsequently, the label vector is embedded into the visual space, which can be used as a correlation filter on the feature maps to capture the latent location of objects. Finally, weperform global average pooling over the output maps and concatenate the features of different levels to produce the hash codes of query images. Extensive experiments on two fine-grained datasets show that the proposed CFH outperforms the state-of-the-art hashing methods.

中文翻译：

---

用于细粒度图像检索的基于相关过滤的散列

用于图像检索的哈希码存储量低、表示能力强，使得哈希技术非常流行。现有的几种深度哈希方法专注于一般图像检索的任务，而忽略了细粒度​​图像检索的任务。最近，一些细粒度的哈希方法被提出来捕捉细微的差异，主要利用单模态视觉特征来解决判别区域定位而忽略语义信息。在这封信中，我们提出了一种相关过滤散列（CFH）方法来学习离散二进制代码，该方法可以充分利用语义信息和视觉特征之间的跨模态相关性进行判别区域定位。具体来说，我们利用特征金字塔网络来学习多级视觉特征。随后，标签向量被嵌入到视觉空间中，它可以用作特征图上的相关过滤器来捕获对象的潜在位置。最后，我们对输出图执行全局平均池化，并连接不同级别的特征以生成查询图像的哈希码。对两个细粒度数据集的大量实验表明，所提出的 CFH 优于最先进的散列方法。我们对输出图执行全局平均池化，并连接不同级别的特征以生成查询图像的哈希码。对两个细粒度数据集的大量实验表明，所提出的 CFH 优于最先进的散列方法。我们对输出图执行全局平均池化，并连接不同级别的特征以生成查询图像的哈希码。对两个细粒度数据集的大量实验表明，所提出的 CFH 优于最先进的散列方法。