上下文感知的深度弱监督图像哈希表示学习方法

针对现有深度监督图像哈希表示学习方法依赖于图像的类别信息,难以在现实中被广泛应用问题,利用与图像相关的标签信息作为监督信息,提出上下文感知的深度弱监督图像哈希表示学习方法。该方法一方面通过自适应捕获图像区域特征的相关上下文来增强它们的表示能力,另一方面通过引入判别损失来提高学习到的哈希码表示的判别性。在现有两个公开数据集上的大量实验结果证明了该方法的有效性。

Context-aware deep weakly supervised image hashing learning method

Existing deep supervised image hashing approaches rely on substantial labeled image data, which is very difficult to be widely applied in reality. By utilizing tags associated with images as the supervision information, a context-aware deep weakly supervised image hashing method was proposed. The method enhanced the image region representations by adaptively capturing the relevant context information of image region features, and raised the discrimination of the learnt hash codes by introducing a discrimination loss. Extensive experiments on two public datasets show the effectiveness of the method.