Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In such a framework, vector quantization is used for coarse partitioning while product quantization is used for quantizing residuals. In the original IVFADC as well as all of its variants, after residuals are computed, the second production quantization step is completely independent of the first vector quantization step. In this work, we seek to exploit the connection between these two steps when we perform non-exhaustive search. More specifically, we decompose a residual vector locally into two orthogonal components and perform uniform quantization and multiscale quantization to each component respectively. The proposed method, called local orthogonal decomposition, combined with multiscale quantization consistently achieves higher recall than previous methods under the same bitrates. We conduct comprehensive experiments on large scale datasets as well as detailed ablation tests, demonstrating effectiveness of our method.
Rotations and codebooks are often applied in IVFADC variations, but there are significant costs associated with them. In the most extreme cases, Locally Optimized Product Quantization (LOPQ) @cite_15 learns a separate rotation matrix and codebook for each partition. This leads to an extra memory cost of @math and @math more multiplications for each query at search time. where @math is the number of VQ partitions we search. When @math and @math increase, the overhead become quickly noticeable and may become even more expensive than ADC itself. For example, when @math , @math , @math , performing the rotation once is as expensive as performing 6,400 ADC computations under an optimized implementation. In practice, it is often desirable to avoid per partition rotation or codebooks, but learn global codebooks and rotation.
@cite_15
2084363474
This paper addresses the problem of learning similarity-preserving binary codes for efficient retrieval in large-scale image collections. We propose a simple and efficient alternating minimization scheme for finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube. This method, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). Our experiments show that the resulting binary coding schemes decisively outperform several other state-of-the-art methods.