Random Exemplar Hashing

We present a new method that addresses the problem of approximate nearest neighbor search via partitioning the feature space. The proposed random exemplar hashing algorithm can be used to generate binary codes of data to facilitate nearest neighbor search within large datasets. Inspired by the idea of using an ensemble of classifiers for discriminative learning, we devise an unsupervised learning algorithm to explore the feature space with respect to randomly selected exemplars. Experimental results on three large datasets show that our method outperforms the state-of-the-art, especially on the cases of longer binary codes.

[1]  Gregory Shakhnarovich,et al.  Boosted Dyadic Kernel Discriminants , 2002, NIPS.

[2]  Luc Van Gool,et al.  Ensemble Partitioning for Unsupervised Image Categorization , 2012, ECCV.

[3]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[4]  Wu-Jun Li,et al.  Isotropic Hashing , 2012, NIPS.

[5]  Benjamin Recht,et al.  Random Features for Large-Scale Kernel Machines , 2007, NIPS.

[6]  Jun Wang,et al.  Self-taught hashing for fast similarity search , 2010, SIGIR.

[7]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[8]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[11]  Patrick Gros,et al.  Asymmetric hamming embedding: taking the best of our bits for large scale image search , 2011, ACM Multimedia.

[12]  Tomasz Malisiewicz,et al.  A Gaussian Approximation of Feature Space for Fast Image Similarity , 2012 .

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

[14]  Cordelia Schmid,et al.  Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search , 2008, ECCV.

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

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

[17]  Mark J. Huiskes,et al.  The MIR flickr retrieval evaluation , 2008, MIR '08.

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

[19]  Kai Li,et al.  Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces , 2008, SIGIR '08.

[20]  Antonio Torralba,et al.  Multidimensional Spectral Hashing , 2012, ECCV.

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

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[23]  Vincent Lepetit,et al.  Randomized trees for real-time keypoint recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Qi Tian,et al.  Super-Bit Locality-Sensitive Hashing , 2012, NIPS.

[25]  Trevor Darrell,et al.  Nearest-Neighbor Methods in Learning and Vision , 2008, IEEE Trans. Neural Networks.

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

[27]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[28]  Jay Yagnik,et al.  The power of comparative reasoning , 2011, 2011 International Conference on Computer Vision.

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

[30]  Eli Shechtman,et al.  In defense of Nearest-Neighbor based image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Svetlana Lazebnik,et al.  Asymmetric Distances for Binary Embeddings , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Geoffrey E. Hinton,et al.  Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.

[33]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[34]  Hanan Samet,et al.  Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling) , 2005 .

[35]  Jonathan Brandt,et al.  Transform coding for fast approximate nearest neighbor search in high dimensions , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[36]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[37]  Trevor Darrell,et al.  Pyramid Match Hashing: Sub-Linear Time Indexing Over Partial Correspondences , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Antonio Torralba,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition , 2022 .