What makes Paris look like Paris?
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Manhattan hashing for large-scale image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Thick boundaries in binary space and their influence on nearest-neighbor search
Pattern Recognition Letters
Comparative evaluation of binary features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Sequential spectral learning to hash with multiple representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Fast and effective retrieval of plant leaf shapes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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
Comparing apples to oranges: a scalable solution with heterogeneous hashing
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Weighted hashing for fast large scale similarity search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A unified approximate nearest neighbor search scheme by combining data structure and hashing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Mixed image-keyword query adaptive hashing over multilabel images
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This paper addresses the problem of learning similarity-preserving binary codes for efficient retrieval in large-scale image collections. We propose a simple and efficient alternating minimization scheme for finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube. This method, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). Our experiments show that the resulting binary coding schemes decisively outperform several other state-of-the-art methods.