Auto-JacoBin: Auto-encoder Jacobian Binary Hashing

Binary codes can be used to speed up nearest neighbor search tasks in large scale data sets as they are efficient for both storage and retrieval. In this paper, we propose a robust auto-encoder model that preserves the geometric relationships of high-dimensional data sets in Hamming space. This is done by considering a noise-removing function in a region surrounding the manifold where the training data points lie. This function is defined with the property that it projects the data points near the manifold into the manifold wisely, and we approximate this function by its first order approximation. Experimental results show that the proposed method achieves better than state-of-the-art results on three large scale high dimensional data sets.

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

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

[3]  Cordelia Schmid,et al.  Improving Bag-of-Features for Large Scale Image Search , 2010, International Journal of Computer Vision.

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

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

[6]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[7]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[9]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[10]  Hanqing Lu,et al.  Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[12]  Chun Chen,et al.  Harmonious Hashing , 2013, IJCAI.

[13]  Prateek Jain,et al.  Fast image search for learned metrics , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Ping Li,et al.  Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) , 2014, NIPS.

[15]  Cordelia Schmid,et al.  Product Quantization for Nearest Neighbor Search , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Dimitris N. Metaxas,et al.  Large Scale Medical Image Search via Unsupervised PCA Hashing , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

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

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

[20]  Gang Hua,et al.  Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Jian Sun,et al.  K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[23]  Pascal Vincent,et al.  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.

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

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

[26]  Brendan McCane,et al.  NOKMeans: Non-Orthogonal K-means Hashing , 2014, ACCV.

[27]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[28]  David J. Fleet,et al.  Cartesian K-Means , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[30]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.

[31]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

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

[33]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[34]  David J. Fleet,et al.  Minimal Loss Hashing for Compact Binary Codes , 2011, ICML.