Pairwise Rotation Hashing for High-dimensional Features
This addresses the problem of scalable nearest neighbor search for high-dimensional data in visual tasks, offering an incremental improvement in efficiency.
The paper tackles the inefficiency of binary hashing for high-dimensional features by proposing a novel sparse linear hashing method based on pairwise rotations, achieving encoding cost of O(n log n) compared to O(n^2) for state-of-the-art methods and comparable or slightly better retrieval accuracy.
Binary Hashing is widely used for effective approximate nearest neighbors search. Even though various binary hashing methods have been proposed, very few methods are feasible for extremely high-dimensional features often used in visual tasks today. We propose a novel highly sparse linear hashing method based on pairwise rotations. The encoding cost of the proposed algorithm is $\mathrm{O}(n \log n)$ for n-dimensional features, whereas that of the existing state-of-the-art method is typically $\mathrm{O}(n^2)$. The proposed method is also remarkably faster in the learning phase. Along with the efficiency, the retrieval accuracy is comparable to or slightly outperforming the state-of-the-art. Pairwise rotations used in our method are formulated from an analytical study of the trade-off relationship between quantization error and entropy of binary codes. Although these hashing criteria are widely used in previous researches, its analytical behavior is rarely studied. All building blocks of our algorithm are based on the analytical solution, and it thus provides a fairly simple and efficient procedure.