The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
Abstract
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 multiclass 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 data set.
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Cites
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“The idea of rotating the data to minimize quantization loss can be found in [17] and [19].”
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Cited by
Deep Hashing Network for Unsupervised Domain Adaptation
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Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478
Learning With Average Precision: Training Image Retrieval With a Listwise Loss
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Central Similarity Quantization for Efficient Image and Video Retrieval
Yuan, L., Wang, T., Zhang, X., Tay, F. E., Jie, Z., Liu, W., & Feng, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00315 · Cited by 413
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
Lin, K., Lu, J., Chen, C.-S., & Zhou, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.133 · Cited by 363
Metadata
- Authors
- Gong, Y., Lazebnik, S., Gordo, A., & Perronnin, F.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-12
- Volume
- 35
- Issue
- 12
- Pages
- 2916-2929
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1974647172
- Cited by
- 1,834
- References
- 5
- FWCI
- 50.82
- Type
- journal article
- Language
- en
- Open access
- closed
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