Despite the great success achieved by prevailing binary local descriptors, they are still suffering from two problems: 1) vulnerable to the geometric transformations; 2) lack of an effective treatment to the highly-correlated bits that are generated by directly applying the scheme of image hashing. To tackle both limitations, we propose an unsupervised Transformation-invariant Binary Local Descriptor learning method (TBLD). Specifically, the transformation invariance of binary local descriptors is ensured by projecting the original patches and their transformed counterparts into an identical high-dimensional feature space and an identical low-dimensional descriptor space simultaneously. Meanwhile, it enforces the dissimilar image patches to have distinctive binary local descriptors. Moreover, to reduce high correlations between bits, we propose a bottom-up learning strategy, termed Adversarial Constraint Module, where low-coupling binary codes are introduced externally to guide the learning of binary local descriptors. With the aid of the Wasserstein loss, the framework is optimized to encourage the distribution of the generated binary local descriptors to mimic that of the introduced low-coupling binary codes, eventually making the former more low-coupling. Experimental results on three benchmark datasets well demonstrate the superiority of the proposed method over the state-of-the-art methods. The project page is available at https://github.com/yoqim/TBLD.

中文翻译：

---

使用低耦合二进制代码学习变换不变局部描述符


尽管流行的二进制局部描述符取得了巨大的成功，但它们仍然面临两个问题：1）容易受到几何变换的影响； 2）对于直接应用图像哈希方案产生的高度相关比特缺乏有效的处理。为了解决这两个限制，我们提出了一种无监督变换不变二进制局部描述符学习方法（TBLD）。具体来说，通过将原始补丁及其变换后的对应部分同时投影到相同的高维特征空间和相同的低维描述符空间中来确保二进制局部描述符的变换不变性。同时，它强制不同的图像块具有独特的二进制局部描述符。此外，为了减少位之间的高相关性，我们提出了一种自下而上的学习策略，称为对抗约束模块，其中外部引入低耦合二进制代码来指导二进制局部描述符的学习。借助 Wasserstein 损失，对框架进行了优化，以鼓励生成的二进制局部描述符的分布模仿引入的低耦合二进制代码的分布，最终使前者的耦合度更低。三个基准数据集的实验结果很好地证明了所提出的方法相对于最先进方法的优越性。该项目页面位于 https://github.com/yoqim/TBLD。