Learning Robust Graph Hashing for Efficient Similarity Search

Unsupervised hashing has recently drawn much attention in efficient similarity search for its desirable advantages of low storage cost, fast search speed, semantic label independence. Among the existing solutions, graph hashing makes a significant contribution as it could effectively preserve the neighbourhood data similarities into binary codes via spectral analysis. However, existing graph hashing methods separate graph construction and hashing learning into two independent processes. This two-step design may lead to sub-optimal results. Furthermore, features of data samples may unfortunately contain noises that will make the built graph less reliable. In this paper, we propose a Robust Graph Hashing (RGH) to address these problems. RGH automatically learns robust graph based on self-representation of samples to alleviate the noises. Moreover, it seamlessly integrates graph construction and hashing learning into a unified learning framework. The learning process ensures the optimal graph to be constructed for subsequent hashing learning, and simultaneously the hashing codes can well preserve similarities of data samples. An effective optimization method is devised to iteratively solve the formulated problem. Experimental results on publicly available image datasets validate the superior performance of RGH compared with several state-of-the-art hashing methods.

[1]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[2]  Kristen Grauman,et al.  Kernelized Locality-Sensitive Hashing , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[4]  Alexandr Andoni,et al.  Optimal Data-Dependent Hashing for Approximate Near Neighbors , 2015, STOC.

[5]  Lei Zhu,et al.  Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval , 2017, IEEE Transactions on Cybernetics.

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

[7]  Jian Wang,et al.  Linear unsupervised hashing for ANN search in Euclidean space , 2016, Neurocomputing.

[8]  Yang Yang,et al.  A Fast Optimization Method for General Binary Code Learning , 2016, IEEE Transactions on Image Processing.

[9]  Xuelong Li,et al.  Large Graph Hashing with Spectral Rotation , 2017, AAAI.

[10]  Lei Zhu,et al.  Learning Compact Visual Representation with Canonical Views for Robust Mobile Landmark Search , 2016, IJCAI.

[11]  Fumin Shen,et al.  Inductive Hashing on Manifolds , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Shih-Fu Chang,et al.  Semi-supervised hashing for scalable image retrieval , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Wei Liu,et al.  Discrete Graph Hashing , 2014, NIPS.

[14]  Minyi Guo,et al.  Supervised hashing with latent factor models , 2014, SIGIR.

[15]  Steven Salzberg,et al.  A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features , 2004, Machine Learning.

[16]  René Vidal,et al.  Sparse subspace clustering , 2009, CVPR.

[17]  Lei Zhu,et al.  Unsupervised multi-graph cross-modal hashing for large-scale multimedia retrieval , 2016, Multimedia Tools and Applications.

[18]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Wei Liu,et al.  Supervised Discrete Hashing , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[21]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[22]  René Vidal,et al.  Structured Sparse Subspace Clustering: A unified optimization framework , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Wu-Jun Li,et al.  Scalable Graph Hashing with Feature Transformation , 2015, IJCAI.

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

[25]  Piotr Indyk,et al.  Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.

[26]  Xuelong Li,et al.  Multi-view Subspace Clustering , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[27]  Lei Zhu,et al.  Online Cross-Modal Hashing for Web Image Retrieval , 2016, AAAI.

[28]  Jiwen Lu,et al.  Deep hashing for compact binary codes learning , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  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).

[30]  Lei Zhu,et al.  Cross-Modal Self-Taught Hashing for large-scale image retrieval , 2016, Signal Process..

[31]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[32]  Nenghai Yu,et al.  Complementary hashing for approximate nearest neighbor search , 2011, 2011 International Conference on Computer Vision.

[33]  Lei Zhu,et al.  Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval , 2017, IEEE Transactions on Knowledge and Data Engineering.

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