Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
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
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to hash: forgiving hash functions and applications
Data Mining and Knowledge Discovery
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
International Journal of Approximate Reasoning
Fast Similarity Search for Learned Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Scalable similarity search with optimized kernel hashing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Product Quantization for Nearest Neighbor Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Composite hashing with multiple information sources
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
No free lunch: brute force vs. locality-sensitive hashing for cross-lingual pairwise similarity
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Hypergraph spectral hashing for similarity search of social image
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Iterative quantization: A procrustean approach to learning binary codes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Compact hashing with joint optimization of search accuracy and time
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A probabilistic model for multimodal hash function learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Complementary hashing for approximate nearest neighbor search
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Effective hashing for large-scale multimedia search
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
Neighbourhood preserving quantisation for LSH
Proceedings of the 36th 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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Hashing is used to learn binary-code representation for data with expectation of preserving the neighborhood structure in the original feature space. Due to its fast query speed and reduced storage cost, hashing has been widely used for efficient nearest neighbor search in a large variety of applications like text and image retrieval. Most existing hashing methods adopt Hamming distance to measure the similarity (neighborhood) between points in the hashcode space. However, one problem with Hamming distance is that it may destroy the neighborhood structure in the original feature space, which violates the essential goal of hashing. In this paper, Manhattan hashing (MH), which is based on Manhattan distance, is proposed to solve the problem of Hamming distance based hashing. The basic idea of MH is to encode each projected dimension with multiple bits of natural binary code (NBC), based on which the Manhattan distance between points in the hashcode space is calculated for nearest neighbor search. MH can effectively preserve the neighborhood structure in the data to achieve the goal of hashing. To the best of our knowledge, this is the first work to adopt Manhattan distance with NBC for hashing. Experiments on several large-scale image data sets containing up to one million points show that our MH method can significantly outperform other state-of-the-art methods.