Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Graphs and Hypergraphs
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Manhattan hashing for large-scale image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
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The development of social media brings great challenges to image retrieval on both efficiency and accuracy. In addition to achieving fast similarity search over large scale data, it is very crucial to represent the complex and high-order relationships among the social contents to improve the semantic understanding of social images.In this paper, unified hypergraph is implemented to model the various relationships among images and other contexts in social media. Moreover, we extend traditional spectral hashing to hypergraph to accelerate similarity search of social images by mapping semantically related vertices into similar binary codes within a short Hamming distance. Furthermore, the proposed HSH approach is extended to out-of-sample data in a supervised manner. We evaluated our approach on the dataset crawled from Flickr and the experiment results indicate that our proposed HSH approach is both efficient and effective.