An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Integration of Stochastic Models by Minimizing α-Divergence
Neural Computation
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
The Journal of Machine Learning Research
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Composite hashing with multiple information sources
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Semi-supervised Discriminant Hashing
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Iterative quantization: A procrustean approach to learning binary codes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Compact hashing with joint optimization of search accuracy and time
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning hash functions for cross-view similarity search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
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Learning to hash involves learning hash functions from a set of images for embedding high-dimensional visual descriptors into a similarity-preserving low-dimensional Hamming space. Most of existing methods resort to a single representation of images, that is, only one type of visual descriptors is used to learn a hash function to assign binary codes to images. However, images are often described by multiple different visual descriptors (such as SIFT, GIST, HOG), so it is desirable to incorporate these multiple representations into learning a hash function, leading to multi-view hashing. In this paper we present a sequential spectral learning approach to multi-view hashing where a hash function is sequentially determined by solving the successive maximization of local variances subject to decorrelation constraints. We compute multi-view local variances by α-averaging view-specific distance matrices such that the best averaged distance matrix is determined by minimizing its α-divergence from view-specific distance matrices. We also present a scalable implementation, exploiting a fast approximate k-NN graph construction method, in which α-averaged distances computed in small partitions determined by recursive spectral bisection are gradually merged in conquer steps until whole examples are used. Numerical experiments on Caltech-256, CIFAR-20, and NUS-WIDE datasets confirm the high performance of our method, in comparison to single-view spectral hashing as well as existing multi-view hashing methods.