Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
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
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Product Quantization for Nearest Neighbor Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Asymmetric distances for binary embeddings
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Thanks to compact data representations and fast similarity computation, many binary code embedding techniques have been recently proposed for large-scale similarity search used in many computer vision applications including image retrieval. Most of prior techniques have centered around optimizing a set of projections for accurate embedding. In spite of active research efforts, existing solutions suffer both from diminishing marginal efficiency as more code bits are used, and high quantization errors naturally coming from the binarization. In order to reduce both quantization error and diminishing efficiency we propose a novel binary code embedding scheme, Quadra-Embedding, that assigns two bits for each projection to define four quantization regions, and a novel binary code distance function tailored specifically to our encoding scheme. Our method is directly applicable to a wide variety of binary code embedding methods. Our scheme combined with four state-of-the-art embedding methods has been evaluated with three public image benchmarks. We have observed that our scheme achieves meaningful accuracy improvement in most experimental configurations under k- and ε-NN search.