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iterative quantization

Iterative quantization is a machine learning technique used to convert continuous, high-dimensional vector representations into compact, similarity-preserving binary codes for efficient nearest-neighbor search and large-scale data retrieval. The method operates on dimensionality-reduced, zero-centered data by searching for an optimal orthogonal rotation matrix that minimizes the quantization error between the rotated feature vectors and the discrete vertices of a binary hypercube. It computes this rotation through an alternating minimization algorithm that repeatedly cycles between assigning data points to their nearest binary vertices and solving an orthogonal Procrustes problem to refine the rotation matrix. By effectively aligning the data distribution with the hypercube axes, iterative quantization preserves relative distances in the resulting binary space while maintaining low computational and memory overhead across both unsupervised and supervised embeddings.

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Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval

Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval

Yunchao Gong, Svetlana Lazebnik, Albert Gordo, Florent Perronnin

OrganizationsUniversitat Autònoma de BarcelonaUniversity of Illinois Urbana-ChampaignUniversity of North Carolina at Chapel HillXerox

Why you should read this

Proposes an alternating minimization method, Iterative Quantization, that finds an optimal orthogonal rotation to align dimensionally-reduced data with the vertices of a binary hypercube, significantly improving retrieval accuracy and scalability for large-scale image search.

This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of 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, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, 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). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset.

Added

2026-09-24