GONG Dahan, CHU Chaoqun, CHEN Kai, et al., “Balanced Hashing,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1204-1209, 2019, doi: 10.1049/cje.2019.08.003
Citation: GONG Dahan, CHU Chaoqun, CHEN Kai, et al., “Balanced Hashing,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1204-1209, 2019, doi: 10.1049/cje.2019.08.003

Balanced Hashing

doi: 10.1049/cje.2019.08.003
Funds:  This work is supported by the National Natural Science Foundation of China (No.61571269, No.61971260).
More Information
  • Corresponding author: GUO Yuchen (corresponding author) received the B.S.degree and Ph.D.degree from School of Software,Tsinghua University,Beijing,China in 2013 and 2018 respectively,and currently is a postdoc researcher in Department of Automation in the same campus.His research interests include multimedia information retrieval,computer vision and machine learning.(Email:yuchen.w.guo@gmail.com)
  • Received Date: 2018-03-21
  • Rev Recd Date: 2018-04-16
  • Publish Date: 2019-11-10
  • Hashing for nearest neighbor search has attracted considerable interest recently given its efficiency in speed and storage. Many methods follow a projection-quantization framework which firstly projects original data into low-dimensional compact space and secondly quantifies each projected dimension to 1 bit by thresholding. The variance of projected dimensions, however, may vary a lot so that quantifying them equivalently degrades the searching performance. In this paper, we put forward a novel method, dubbed Balanced hashing (BH), which finds adjustment functions to reproject the data such that the variance of dimensions can be balanced by directly and explicitly maximizing the degree of balance of data, while preserving important properties. Experiments on benchmarks demonstrate that BH can outperform several state-of-the-art methods.
  • loading
  • A. Andoni and P. Indyk, "Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions", FOCS'06, 2006.
    Y. Guo, G. Ding, X. Jin, et al., "Learning predictable and discriminative attributes for visual recognition", AAAI Conference on Artificial Intelligence, pp.3783-3789, 2015.
    B. Kulis and K. Grauman, "Kernelized locality-sensitive hashing for scalable image search", IEEE International Conference on Computer Vision, Vol.9, pp.2130-2137, 2009.
    D. Zhang, J. Wang, D. Cai, et al., "Self-taught hashing for fast similarity search", Proceeding of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.18-25, 2010.
    Y. Weiss, A. Torralba and R. Fergus, "Spectral hashing", in Advances in Neural Information Processing Systems, pp.1753-1760, 2009.
    R. Salakhutdinov and G. Hinton, "Semantic hashing", IJAR, Vol.50, No.7, pp.969-978, 2009.
    J. Wang, S. Kumar and S.-F. Chang, "Semi-supervised hashing for scalable image retrieval", IEEE Conf. on Computer Vision and Pattern Recognition, pp.3424-3431, 2010.
    W. Liu, J. Wang, S. Kumar, et al., "Hashing with graphs", International Conference on Machine Learning, pp.1-8, 2011.
    G. Ding, Y. Guo and J. Zhou, "Collective matrix factorization hashing for multimodal data", IEEE Conf. on Computer Vision and Pattern Recognition, pp.2075-2082, 2014.
    Y. Gong and S. Lazebnik, "Iterative quantization:A procrustean approach to learning binary codes", IEEE Conference on Computer Vision and Pattern Recognition, pp.817-824, 2011.
    W. Kong and W.-J. Li, "Double-bit quantization for hashing", AAAI Conf. on Artificial Intelligence, pp.634-640, 2012.
    Y. Guo, G. Ding, L. Liu, et al., "Learning to hash with optimized anchor embedding for scalable retrieval", IEEE Trans. Image Processing, Vol.26, No.3, pp.1344-1354, 2017.
    Z. Li and J. Tang, "Weakly supervised deep matrix factorization for social image understanding", IEEE Trans. Image Processing, Vol.26, No.1, pp.276-288, 2017.
    J. Tang, Z. Li, M. Wang, et al., "Neighborhood discriminant hashing for large-scale image retrieval", IEEE Trans. Image Processing, Vol.24, No.9, pp.2827-2840, 2015.
    Z. Li and J. Tang, "Weakly supervised deep metric learning for community-contributed image retrieval", IEEE Trans. Multimedia, Vol.17, No.11, pp.1989-1999, 2015.
    Y. Guo, G. Ding and J. Han, "Robust quantization for general similarity search", IEEE Trans. Image Processing, Vol.27, No.2, pp.949-963, 2018.
    G. Ding, Y. Guo, J. Zhou, et al., "Large-scale cross-modality search via collective matrix factorization hashing", IEEE Trans. Image Processing, Vol.25, No.11, pp.5427-5440, 2016.
    Y. Guo, G. Ding, J. Han, et al., "Zero-shot learning with transferred samples", IEEE Trans. Image Processing, Vol.26, No.7, pp.3277-3290, 2017.
    W. Kong and W.-J. Li, "Isotropic hashing", in Advances in Neural Information Processing Systems, pp.1646-1654, 2012.
    B. Xu, J. Bu, Y. Lin, et al., "Harmonious hashing", in International Joint Conference on Artificial Intelligence, pp.1820-1826, 2013.
    K. Zhao, H. Lu and J. Mei, "Locality preserving hashing", AAAI Conf. on Artificial Intelligence, pp.2874-2880, 2014.
    D. Cai, X. He, J. Han, et al., "Graph regularized nonnegative matrix factorization for data representation", IEEE Trans. Pattern Anal. Mach. Intell., Vol.33, No.8, pp.1548-1560, 2011.
    S. Kumar and R. Udupa, "Learning hash functions for crossview similarity search", International Joint Conference on Artificial Intelligence, pp.1360-1365, 2011.
    D. Zhang, F. Wang and L. Si, "Composite hashing with multiple information sources", Proceeding of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.225-234, 2011.
    S. Kim, Y. Kang and S. Choi, "Sequential spectral learning to hash with multiple representations", European Conference on Computer Vision. Springer, pp.538-551, 2012.
    J. Song, Y. Yang, Y. Yang, et al., "Intermedia hashing for large-scale retrieval from heterogeneous data sources", in ICMD. ACM, pp.785-796, 2013.
    K. Zhao, D. Liu and H. Lu, "Local linear spectral hashing", in Neural Information Processing ICONIP, pp.283-290, 2013.
    L. Chen, D. Xu, I. W. Tsang, et al., "Spectral embedded hashing for scalable image retrieval", IEEE T. Cybernetics, Vol.44, No.7, pp.1180-1190, 2014.
    K. He, F. Wen and J. Sun, "K-means hashing:An affinitypreserving quantization method for learning binary compact codes", IEEE Conference on Computer Vision and Pattern Recognition, pp.2938-2945, 2013.
    H. Jegou, M. Douze, C. Schmid, et al., "Aggregating local descriptors into a compact image representation", IEEE Conference on Computer Vision and Pattern Recognition, pp.3304-3311, 2010.
    Z. Wen and W. Yin, "A feasible method for optimization with orthogonality constraints", Math. Program., Vol.142, No.1-2, pp.397-434, 2013.
    J. Nocedal and S. Wright, Numerical Optimization, Springer Science & Business Media, 1999.
    Y. Yang, H. T. Shen, Z. Ma, et al., "ℓ2,1-norm regularized discriminative feature selection for unsupervised learning", in International Joint Conference on Artificial Intelligence, pp.1589-1594, 2011.
    A. Krizhevsky, "Learning multiple layers of features from tiny images", Tech Report, Univ. of Toronto, 2009.
    A. Oliva and T. Torralba, "Modeling the shape of the scene:A holistic representation of the spatial envelope", IJCV, Vol.42, No.3, pp.145-175, 2001.
    G. Griffin, A. Hulob and P. Perona, "Caltech-256 object category dataset", Tech Report, Caltech, 2007.
    J. Wang, J. Yang, K. Yu, et al., "Locality-constrained linear coding for image classification", The IEEE Conf. on Computer Vision and Pattern Recognition, pp.3360-3367, 2010.
    D. G. Lowe, "Distinctive image features from scale-invariant keypoints", IJCV, Vol.60, No.2, pp.91-110, 2004.
    T. Chua, J. Tang, R. Hong, et al., "NUS-WIDE:A real-world web image database from national university of singapore", in Proceedings of the ACM CIVR, Page 48, 2009.
    J. Zhou, G. Ding and Y. Guo, "Latent semantic sparse hashing for cross-modal similarity search", Proceeding of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.415-424, 2014.
    Y. Zhen and D.-Y. Yeung, "Co-regularized hashing for multimodal data", in Advances in Neural Information Processing Systems, pp.1376-1384, 2012.
    Y. Zhen and D. Yeung, "A probabilistic model for multimodal hash function learning", the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.940-948, 2012.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (438) PDF downloads(162) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return