This work proposes LDVS, a learnable binary local descriptor devised for matching natural images within the MPEG CDVS framework. LDVS descriptors are learned so that they can be sign-quantized and compared using the Hamming distance. The underlying convolutional architecture enjoys a moderate parameters count for operations on mobile devices. Our experiments show that LDVS descriptors perform favorably over comparable learned binary descriptors at patch matching on two different datasets. A complete pair-wise image matching pipeline is then designed around LDVS descriptors, integrating them in the reference CDVS evaluation framework. Experiments show that LDVS descriptors outperform the compressed CDVS SIFT-like descriptors at pair-wise image matching over the challenging CDVS image dataset.

中文翻译：

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可视搜索的可学习描述符

这项工作提出了LDVS，这是一种可学习的二进制本地描述符，用于在MPEG CDVS框架内匹配自然图像。学习LDVS描述符，以便可以使用汉明距离对它们进行符号量化和比较。基本的卷积体系结构在移动设备上的操作中享有中等的参数数量。我们的实验表明，在两个不同数据集的补丁匹配中，LDVS描述符的性能优于可比较的学习型二进制描述符。然后围绕LDVS描述符设计一个完整的成对图像匹配管道，并将其集成到参考CDVS评估框架中。实验表明，在具有挑战性的CDVS图像数据集上的成对图像匹配中，LDVS描述符的性能优于压缩的CDVS SIFT类描述符。