Ternary Hashing
This work addresses the need for more efficient and effective retrieval systems in machine learning, particularly for image datasets, though it is incremental as it builds upon existing binary hashing methods.
The paper tackles the problem of improving retrieval efficiency and performance in learning to hash methods by introducing a novel ternary hash encoding, which achieves consistent improvements in mean average precision (mAP) ranging from 1% to 5.9% on datasets like CIFAR10, NUS-WIDE, and ImageNet100 compared to state-of-the-art binary hashing.
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 Łukasiewicz 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.