This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several (M) elements, each of which is selected from a different dictionary, to accurately approximate a D-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the distance computation is reduced to O(M) from O(D) through a distance table lookup scheme. The resulting approach is called near-orthogonal composite quantization. We theoretically justify the equivalence between near-orthogonal composite quantization and minimizing an upper bound of a function formed by jointly considering the quantization error and the search cost according to a generalized triangle inequality. We empirically show the efficacy of the proposed approach over several benchmark datasets. In addition, we demonstrate the superior performances in other three applications: combination with inverted multi-index, inner-product similarity search, and query compression for mobile search.

中文翻译：

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 复合量化


本文研究了近似最近邻搜索的紧凑编码方法。我们引入了复合量化框架。它使用几个（M）元素的组合，每个元素都选自不同的字典，以精确地逼近D维向量，从而产生精确的搜索，并通过由元素的索引组成的短代码来表示数据向量。相应字典中选定的元素。我们的关键贡献在于引入了近正交约束，这使得搜索效率得到保证，因为通过距离表查找方案将距离计算的成本从 O(D) 减少到 O(M)。由此产生的方法称为近正交复合量化。我们从理论上证明了近正交复合量化与根据广义三角不等式共同考虑量化误差和搜索成本而形成的函数上限最小化之间的等价性。我们凭经验证明了所提出的方法在几个基准数据集上的有效性。此外，我们还展示了其他三个应用程序的优越性能：与倒排多索引、内积相似性搜索和移动搜索的查询压缩相结合。