Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Scalable similarity search with optimized kernel hashing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Compact hashing for mixed image-keyword query over multi-label images
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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)
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Mixed image-keyword query adaptive hashing over multilabel images
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multiple feature kernel hashing for large-scale visual search
Pattern Recognition
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Hashing methods, which generate binary codes to preserve certain similarity, recently have become attractive in many applications like large scale visual search. However, most of state-of-the-art hashing methods only utilize single feature type, while combining multiple features has been proved very helpful in image search. In this paper we propose a novel hashing approach that utilizes the information conveyed by different features. The multiple feature hashing can be formulated as a similarity preserving problem with optimal linearly-combined multiple kernels. Such formulation is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also helpful to achieve fast training and search. We present an efficient alternating optimization to learn the hashing functions and the optimal kernel combination. Experimental results on two well-known benchmarks CIFAR-10 and NUS-WIDE show that the proposed method can achieve 11% and 34% performance gains over state-of-the-art methods.