The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Optimized Product Quantization
Abstract
Product quantization (PQ) is an effective vector quantization method. A product quantizer can generate an exponentially large codebook at very low memory/time cost. The essence of PQ is to decompose the high-dimensional vector space into the Cartesian product of subspaces and then quantize these subspaces separately. The optimal space decomposition is important for the PQ performance, but still remains an unaddressed issue. In this paper, we optimize PQ by minimizing quantization distortions w.r.t the space decomposition and the quantization codebooks. We present two novel solutions to this challenging optimization problem. The first solution iteratively solves two simpler sub-problems. The second solution is based on a Gaussian assumption and provides theoretical analysis of the optimality. We evaluate our optimized product quantizers in three applications: (i) compact encoding for exhaustive ranking [1], (ii) building inverted multi-indexing for non-exhaustive search [2], and (iii) compacting image representations for image retrieval [3]. In all applications our optimized product quantizers outperform existing solutions.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,319
Approximate nearest neighbors: towards removing the curse of dimensionality
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Scalable Recognition with a Vocabulary Tree
Nister, D., & Stewenius, H. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.264 · Cited by 3,579
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 10×
“In the exhaustive search, the data are quantized into codewords [1], [8], [9]; the distances of vectors are approximated by the distances of codewords.”
BackgroundMethodInfluentialAggregating local descriptors into a compact image representation
Jegou, H., Douze, M., Schmid, C., & Perez, P. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540039 · Cited by 2,692
Cited by
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Exploring the Limits of Weakly Supervised Pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_12 · Cited by 1,224
Self-citation(2021) · doi:10.1145/3448016.3457550 · Cited by 416
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization
Lynen, S., Sattler, T., Bosse, M., Hesch, J., Pollefeys, M., & Siegwart, R. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.037 · Cited by 243
A Survey on Deep Hashing Methods
Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., & Hua, X.-S. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532624 · Cited by 241
Metadata
- Authors
- Ge, T., He, K., Ke, Q., & Sun, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2014-04
- Volume
- 36
- Issue
- 4
- Pages
- 744-755
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2077815765
- Cited by
- 391
- References
- 36
- FWCI
- 23.15
- Type
- journal article
- Language
- en
- Open access
- closed
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