Nearest Neighbor Search Based on Bit String Partition and Multiple Index

被引:0
作者
Miao J. [1 ]
Li Z. [1 ]
Zhou Z. [1 ]
Yang C. [1 ]
Liu Z. [1 ]
Liu W. [1 ]
机构
[1] School of Information Science and Technology, Dalian Maritime University, Dalian
来源
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 05期
关键词
Bit string partition; Hash representation; Multi-table index; Nearest neighbor search; Query expansion;
D O I
10.3724/SP.J.1089.2019.17341
中图分类号
学科分类号
摘要
The bit string represented by hash was one of the most effective methods to solve the similarity search problem of massive data. In view of the problem that the bit string indexing method lead to low search effect, a neighbor search algorithm based on bit string partitioning and multiple index is proposed. Firstly, the essence of bit string partitioning is a combinatorial optimization problem. In this paper, a greedy idea is used to give an approximate solution to the problem. Secondly, in the neighborhood query phase, a new query expansion and fusion mechanism is proposed based on the multi-index structure. Finally, a query adaptive method is used to optimize the imbalance between multiple indexes. On the MNIST, CIFAR-10, SIFT-1M and GIST-1M datasets, experiments and results are presented using Matlab software. The results demonstrate that the proposed method is effective and general in neighborhood search on the index structure based on hash representation. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码:771 / 779
页数:8
相关论文
共 24 条
  • [11] Gong Y.C., Lazebnik S., Gordo A., Et al., Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 12, pp. 2916-2929, (2013)
  • [12] Song J.K., Gao L.L., Liu L., Et al., Quantization-based hashing: a general framework for scalable image and video retrieval, Pattern Recognition, 75, pp. 175-187, (2018)
  • [13] Liu X.L., Deng C., Mu Y.D., Et al., Boosting complementary hash tables for fast nearest neighbor search, Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 4183-4189, (2017)
  • [14] Fu Q., Han X., Liu X.L., Et al., Complementary binary quantization for joint multiple indexing, Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 2114-2120, (2018)
  • [15] Li W.J., Wang S., Kang W.C., Feature learning based deep supervised hashing with pairwise labels, Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 1711-1717, (2016)
  • [16] Jiang Q.Y., Li W.J., Asymmetric deep supervised hashing, Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 3342-3349, (2018)
  • [17] Liu W., Ma H.D., Qi H., Et al., Deep learning hashing for mobile visual search, EURASIP Journal on Image and Video Processing, 17, (2017)
  • [18] Song J.K., He T., Gao L.L., Et al., Binary generative adversarial networks for image retrieval, Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 394-401, (2018)
  • [19] Liu X.L., He J.F., Lang B., Et al., Hash bit selection: a unified solution for selection problems in hashing, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1570-1577, (2013)
  • [20] Pavan M., Pelillo M., Dominant sets and pairwise clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1, pp. 167-172, (2007)