SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
ACM Transactions on Graphics (TOG)
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Metric-Based Shape Retrieval in Large Databases
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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)
Octree-Related Data Structures and Algorithms
IEEE Computer Graphics and Applications
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Visual Search of Videos Cast as Text Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
The Journal of Machine Learning Research
Scalable similarity search with optimized kernel hashing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
Fast approximate similarity search based on degree-reduced neighborhood graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
The anchors hierarchy: using the triangle inequality to survive high dimensional data
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Fast approximate nearest-neighbor search with k-nearest neighbor graph
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Scalable k-NN graph construction for visual descriptors
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Complementary hashing for approximate nearest neighbor search
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Similar image search with a tiny bag-of-delegates representation
Proceedings of the 20th ACM international conference on Multimedia
Scalable similar image search by joint indices
Proceedings of the 20th ACM international conference on Multimedia
Similar image search with a tiny bag-of-delegates representation
Proceedings of the 20th ACM international conference on Multimedia
Scalable similar image search by joint indices
Proceedings of the 20th ACM international conference on Multimedia
Image search by graph-based label propagation with image representation from DNN
Proceedings of the 21st ACM international conference on Multimedia
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In this paper, we address the approximate nearest neighbor (ANN) search problem over large scale visual descriptors. We investigate a simple but very effective approach, neighborhood graph search, which constructs a neighborhood graph to index the data points and conducts a local search, expanding neighborhoods with a best-first manner, for ANN search. Our empirical analysis shows that neighborhood expansion is very efficient, with O(1) cost, for a new NN candidate location, and has high chances to locate true NNs and hence it usually performs well. However, it often gets sub-optimal solutions since local search only checks the neighborhood of the current solution, or conducts exhaustive and continuous neighborhood expansions to find better solutions, which deteriorates the query efficiency. In this paper, we propose a query-driven iterated neighborhood graph search approach to improve the performance. We follow the iterated local search (ILS) strategy, widely-used in combinatorial optimization, to find a solution beyond a local optimum. We handle the key challenge in making neighborhood graph search adapt to ILS, Perturbation, which generates a new pivot to restart a local search. To this end, we present a criterion to check if the local search over a neighborhood graph arrives at the local solution. Moreover, we exploit the query and search history to design the perturbation scheme, resulting in a more effective search. The major benefit is avoiding unnecessary neighborhood expansions and hence more efficiently finding true NNs. Experimental results on large scale SIFT matching, similar image search, and shape retrieval with non-metric distance measures, show that our approach performs much better than previous state-of-the-art ANN search approaches.