Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Min-wise independent permutations
Journal of Computer and System Sciences - 30th annual ACM symposium on theory of computing
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently matching sets of features with random histograms
MM '08 Proceedings of the 16th ACM international conference on Multimedia
International Journal of Approximate Reasoning
Concept representation based video indexing
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Locality sensitive hashing: A comparison of hash function types and querying mechanisms
Pattern Recognition Letters
Scalable similarity search with optimized kernel hashing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Randomized locality sensitive vocabularies for bag-of-features model
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Compact hashing for mixed image-keyword query over multi-label images
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Dual local consistency hashing with discriminative projections selection
Signal Processing
Mixed image-keyword query adaptive hashing over multilabel images
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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In recent years, locality-sensitive hashing (LSH) has gained plenty of attention from both the multimedia and computer vision communities due to its empirical success and theoretic guarantee in large-scale visual indexing and retrieval. Conventional LSH algorithms are designated either for generic metrics such as Cosine similarity, ℓ2-norm and Jaccard index, or for the metrics learned from user-supplied supervision information. The common drawbacks of existing algorithms are their incapability to be adapted to metric changes, along with the inefficacy when handling diverse semantics (e. g., more than 1K different categories in the well-known ImageNet database). For the metrics underlying the hashing structure, even tiny changes tend to nullify previous indexing efforts, which motivates our proposed framework towards "reconfigurable hashing". The basic idea is to maintain a large pool of over-complete hashing functions embedded in the ambient feature space, which serves as the common infrastructure of high-level diverse semantics. At the runtime, the algorithm dynamically selects relevant hashing bits by maximizing the consistency to specific semantics-induced metric, thereby achieving reusability of the pre-computed hashing bits. Such a reusable scheme especially benefits the indexing and retrieval of large-scale dataset, since it facilitates one-off indexing rather than continuous computation-intensive maintenance towards metric adaptation. We propose a sequential bit-selection algorithm based on local consistency and global regularization. Extensive studies are conducted on large-scale image benchmarks to comparatively investigate the performance of different strategies on reconfigurable hashing. Despite the vast literature on hashing, to our best knowledge rare endeavors have been spent toward the reusability of hashing structures in large-scale datasets.