Machine Learning - Special issue on inductive transfer
Min-wise independent permutations (extended abstract)
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Multidimensional binary search trees used for associative searching
Communications of the ACM
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
A shared-subspace learning framework for multi-label classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
Scalable similarity search with optimized kernel hashing
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning reconfigurable hashing for diverse semantics
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Composite hashing with multiple information sources
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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 for mixed image-keyword query over multi-label images
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Weak attributes for large-scale image retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Supervised hashing with kernels
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Compact kernel hashing with multiple features
Proceedings of the 20th ACM international conference on Multimedia
Semi-Supervised Hashing for Large-Scale Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This article defines a new hashing task motivated by real-world applications in content-based image retrieval, that is, effective data indexing and retrieval given mixed query (query image together with user-provided keywords). Our work is distinguished from state-of-the-art hashing research by two unique features: (1) Unlike conventional image retrieval systems, the input query is a combination of an exemplar image and several descriptive keywords, and (2) the input image data are often associated with multiple labels. It is an assumption that is more consistent with the realistic scenarios. The mixed image-keyword query significantly extends traditional image-based query and better explicates the user intention. Meanwhile it complicates semantics-based indexing on the multilabel data. Though several existing hashing methods can be adapted to solve the indexing task, unfortunately they all prove to suffer from low effectiveness. To enhance the hashing efficiency, we propose a novel scheme “boosted shared hashing”. Unlike prior works that learn the hashing functions on either all image labels or a single label, we observe that the hashing function can be more effective if it is designed to index over an optimal label subset. In other words, the association between labels and hash bits are moderately sparse. The sparsity of the bit-label association indicates greatly reduced computation and storage complexities for indexing a new sample, since only limited number of hashing functions will become active for the specific sample. We develop a Boosting style algorithm for simultaneously optimizing both the optimal label subsets and hashing functions in a unified formulation, and further propose a query-adaptive retrieval mechanism based on hash bit selection for mixed queries, no matter whether or not the query words exist in the training data. Moreover, we show that the proposed method can be easily extended to the case where the data similarity is gauged by nonlinear kernel functions. Extensive experiments are conducted on standard image benchmarks like CIFAR-10, NUS-WIDE and a-TRECVID. The results validate both the sparsity of the bit-label association and the convergence of the proposed algorithm, and demonstrate that the proposed hashing scheme achieves substantially superior performances over state-of-the-art methods under the same hash bit budget.