A Unified Approximate Nearest Neighbor Search Scheme by Combining Data Structure and Hashing

Nowadays, Nearest Neighbor Search becomes more and more important when facing the challenge of big data. Traditionally, to solve this problem, researchers mainly focus on building effective data structures such as hierarchical k-means tree or using hashing methods to accelerate the query process. In this paper, we propose a novel unified approximate nearest neighbor search scheme to combine the advantages of both the effective data structure and the fast Hamming distance computation in hashing methods. In this way, the searching procedure can be further accelerated. Computational complexity analysis and extensive experiments have demonstrated the effectiveness of our proposed scheme.

[1]  Svetlana Lazebnik,et al.  Iterative quantization: A procrustean approach to learning binary codes , 2011, CVPR 2011.

[2]  Edgar Chávez,et al.  Using the k-Nearest Neighbor Graph for Proximity Searching in Metric Spaces , 2005, SPIRE.

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

[4]  Svetlana Lazebnik,et al.  Locality-sensitive binary codes from shift-invariant kernels , 2009, NIPS.

[5]  Ada Wai-Chee Fu,et al.  Enhanced nearest neighbour search on the R-tree , 1998, SGMD.

[6]  Shih-Fu Chang,et al.  Spherical hashing , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  Daniel Gooch,et al.  Communications of the ACM , 2011, XRDS.

[9]  Wei Liu,et al.  Hashing with Graphs , 2011, ICML.

[10]  Jiehua Zhu,et al.  National Natural Science Foundation of China (NSFC) , 2013 .

[11]  Olivier Buisson,et al.  Random maximum margin hashing , 2011, CVPR 2011.

[12]  David J. Fleet,et al.  Fast search in Hamming space with multi-index hashing , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  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).

[14]  Zi Huang,et al.  Multiple feature hashing for real-time large scale near-duplicate video retrieval , 2011, ACM Multimedia.

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

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

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

[18]  Shih-Fu Chang,et al.  Semi-Supervised Hashing for Large-Scale Search , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Rongrong Ji,et al.  Supervised hashing with kernels , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Shih-Fu Chang,et al.  Sequential Projection Learning for Hashing with Compact Codes , 2010, ICML.

[21]  Adam Meyerson,et al.  Fast and Accurate k-means For Large Datasets , 2011, NIPS.

[22]  Kristen Grauman,et al.  Kernelized locality-sensitive hashing for scalable image search , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Yasin Abbasi-Yadkori,et al.  Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph , 2011, IJCAI.

[24]  Philip H. S. Torr,et al.  Efficient discriminative learning of parametric nearest neighbor classifiers , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[26]  H. Damasio,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .