Optimized Codebook Construction and Assignment for Product Quantization-based Approximate Nearest Neighbor Search

Nearest neighbor search (NNS) among large-scale and high-dimensional vectors has played an important role in recent large-scale multimedia search applications. This paper proposes an optimized codebook construction algorithm for approximate NNS based on product quantization. The proposed algorithm iteratively optimizes both codebooks for product quantization and an assignment table that indicates the optimal codebook in product quantization. In experiments, the proposed method is shown to achieve better accuracy in approximate NNS than the conventional method with the same memory requirement and the same computational cost. Furthermore, use of a larger number of codebooks increases the accuracy of approximate NNS at the expense of a slight increase in the memory requirement.

[1]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[3]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[4]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[5]  Trevor Darrell,et al.  Learning to Hash with Binary Reconstructive Embeddings , 2009, NIPS.

[6]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[7]  Jonathan Brandt,et al.  Transform coding for fast approximate nearest neighbor search in high dimensions , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Antonio Torralba,et al.  Spectral Hashing , 2008, NIPS.

[9]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[11]  Cordelia Schmid,et al.  Product Quantization for Nearest Neighbor Search , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Michael Isard,et al.  Lost in quantization: Improving particular object retrieval in large scale image databases , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Richard I. Hartley,et al.  Optimised KD-trees for fast image descriptor matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[16]  Antonio Torralba,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition , 2022 .

[17]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[18]  Hongbin Zha,et al.  Optimizing kd-trees for scalable visual descriptor indexing , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[20]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).