Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
International Journal of Approximate Reasoning
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A globally optimal approach for 3D elastic motion estimation from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Iterative quantization: A procrustean approach to learning binary codes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Semi-Supervised Hashing for Large-Scale Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic hashing using tags and topic modeling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Similarity search, or finding approximate nearest neighbors, is an important technique for many applications. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency. However, most existing hashing methods treat all hashing bits equally and the distance between data examples is calculated as the Hamming distance between their hashing codes, while different hashing bits may carry different amount of information. This paper proposes a novel method, named Weighted Hashing (WeiHash), to assign different weights to different hashing bits. The hashing codes and their corresponding weights are jointly learned in a unified framework by simultaneously preserving the similarity between data examples and balancing the variance of each hashing bit. An iterative coordinate descent optimization algorithm is designed to derive desired hashing codes and weights. Extensive experiments on two large scale datasets demonstrate the superior performance of the proposed research over several state-of-the-art hashing methods.