An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Introduction to algorithms
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
A fast learning algorithm for deep belief nets
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
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Principles of hash-based text retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
International Journal of Approximate Reasoning
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning reconfigurable hashing for diverse semantics
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Lost in binarization: query-adaptive ranking for similar image search with compact codes
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
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Semantic hashing is a promising way to accelerate similarity search, which designs compact binary codes for a large number of images so that semantically similar images are mapped to close codes. Retrieving similar neighbors is then simply accomplished by retrieving images that have codes within a small Hamming distance of the code of the query. However, most of the existing hashing approaches, such as spectral hashing (SH), learn the binary codes by preserving the global similarity, which do not have full discriminative power. In this paper, we propose a dual local consistency hashing method which not only makes the similar images have the same codes but also dissimilar images with different codes. Moreover, we propose a PCA projection selecting scheme that choose the most discriminative projection for each bit of the codes. Therefore, the binary codes learned by our approach are more powerful and discriminative for similarity search. Extensive experiments are conducted on publicly available datasets and the comparison results demonstrate that our approach can outperform the state-of-art methods.