An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Data-oriented locality sensitive hashing
Proceedings of the international conference on Multimedia
Product Quantization for Nearest Neighbor Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient binary code indexing with pivot based locality sensitive clustering
Multimedia Tools and Applications
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Nearest neighbor search in Euclidean space is a fundamental problem in multimedia retrieval. The difficulty of exact nearest neighbor search has led to approximate solutions that sacrifice precision for efficiency. Among such solutions, approaches that embed data into binary codes in Hamming space have gained significant success for their efficiency and practical memory requirements. However, binary code searching only finds a big and coarse set of similar neighbors in Hamming space, and hence expensive Euclidean distance based ranking of the coarse set is needed to find nearest neighbors. Therefore, to improve nearest neighbor search efficiency, we proposed a novel binary code method called Integrated Binary Code (IBC) to get a compact set of similar neighbors. Experiments on public datasets show that our method is more efficient and effective than state-of-the-art in approximate nearest neighbor search.