Joint hashing feature and classifier learning for cross-modal retrieval

被引：0
|
作者：
Liu H. [1 ]
Wu X. [1 ]
Yu J. [1 ]
机构：
[1] Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Classifier Learning; Cross-Modal Retrieval; Hashing Learning; Matrix Factorization;
D O I：
10.16451/j.cnki.issn1003-6059.202002008
中图分类号：
学科分类号：
摘要：
To solve the problem of low retrieval accuracy and long training time in cross-modal retrieval algorithms, a cross-modal retrieval algorithm joining hashing feature and classifier learning(HFCL) is proposed. Uniform hash codes are utilized to describe different modal data with the same semantics. In the training stage, label information is utilized to study discriminative hash codes. And the kernel logistic regression is adopted to learn the hash function of each modal. In the testing stage, for any sample, the hash feature is generated by learned hash function, and another modal datum related to its semantics is retrieved from the database. Experiments on three public datasets verify the effectiveness of HFCL. © 2020, Science Press. All right reserved.
引用
下载
收藏
页码：160 / 165
页数：5
相关论文
共 18 条
  • [1] Liu H., Ji R.R., Wu Y.J., Et al., Cross-Modality Binary Code Learning via Fusion Similarity Hashing, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6345-6353, (2017)
  • [2] Song J.K., Yang Y., Yang Y., Et al., Inter-Media Hashing for Large-Scale Retrieval from Heterogeneous Data Sources, Proc of the ACM SIGMOD International Conference on Management of Data, pp. 785-796, (2013)
  • [3] Zhang H., Wu F., Zhuang Y.T., Cross-Media Retrieval Method Based on Feature Subspace Learning, Pattern Recognition and Artificial Intelligence, 21, 6, pp. 739-745, (2008)
  • [4] Shen X.B., Shen F.M., Sun Q.S., Et al., Robust Cross-View Hashing for Multimedia Retrieval, IEEE Signal Processing Letters, 23, 6, pp. 893-897, (2016)
  • [5] Zhuang L., Wang C., Zhou F., Et al., Correlation Space Embedding Algorithm and Its Application to Image Retrieval, Pattern Recognition and Artificial Intelligence, 27, 4, pp. 363-371, (2014)
  • [6] Yu Y., Wu X.J., Kittler J., Semi-supervised Hashing for Semi-Paired Cross-View Retrieval, Proc of the 24th International Conference on Pattern Recognition, pp. 958-963, (2018)
  • [7] Zhu X.F., Huang Z., Shen H.T., Et al., Linear Cross-Modal Hashing for Efficient Multimedia Search, Proc of the 21st ACM International Conference on Multimedia, pp. 143-152, (2013)
  • [8] Zheng F., Tang Y., Shao L., Hetero-Manifold Regularisation for Cross-Modal Hashing, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 5, pp. 1059-1071, (2018)
  • [9] Ding G.G., Guo Y.C., Zhou J., Collective Matrix Factorization Hashing for Multimodal Data, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4321-4328, (2014)
  • [10] Zhou J.L., Ding G.G., Guo Y.C., Latent Semantic Sparse Hashing for Cross-Modal Similarity Search, Proc of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 415-424, (2014)
12