Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Multidimensional binary search trees used for associative searching
Communications of the ACM
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A posteriori multi-probe locality sensitive hashing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Evaluation of GIST descriptors for web-scale image search
Proceedings of the ACM International Conference on Image and Video Retrieval
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Scalable similarity search with optimized kernel hashing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Composite hashing with multiple information sources
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Domain Transfer Multiple Kernel Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compact hashing for mixed image-keyword query over multi-label images
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Weak attributes for large-scale image retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Mobile product search with Bag of Hash Bits and boundary reranking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Affinity aggregation for spectral clustering
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Compact kernel hashing with multiple features
Proceedings of the 20th ACM international conference on Multimedia
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Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods.