This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts. Two kinds of axiomatic ternary logic, Kleene logic and {\L}ukasiewicz logic are adopted to calculate the Ternary Hamming Distance (THD) for both the learning/encoding and testing/querying phases. Our work demonstrates that, with an efficient implementation of ternary logic on standard binary machines, the proposed ternary hashing is compared favorably to the binary hashing methods with consistent improvements of retrieval mean average precision (mAP) ranging from 1\% to 5.9\% as shown in CIFAR10, NUS-WIDE and ImageNet100 datasets.

翻译：本文提出了一种用于学习散列方法的新颖的永久散列编码方法,它提供了一种原则性更有效的编码方法,其性能优于最先进的二进制散列对等工艺的性能。采用了两种不言而喻的半成式逻辑,即Kleene逻辑和 {L}ukasiewicz逻辑,用于计算学习/编码和测试/征服阶段的Ternary Hamming距离(THD) 。我们的工作表明,随着在标准双进制机器上有效采用长期逻辑,拟议的长质散列法与二进制散列法相比,比较优于一致改进的检索平均平均精确度(mAP),范围从1 ⁇ 至5.9 ⁇ 不等,如CIFAR10、NUS-WIDE和图像网100数据集所示。