Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Atomic Decomposition by Basis Pursuit
SIAM Review
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Semi-Supervised Classification Using Linear Neighborhood Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
The Journal of Machine Learning Research
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Efficient Euclidean projections in linear time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Efficient large-scale image annotation by probabilistic collaborative multi-label propagation
Proceedings of the international conference on Multimedia
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Mobile product search with Bag of Hash Bits and boundary reranking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Multi-label visual classification with label exclusive context
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Nowadays, content-based retrieval methods are still the development trend of the traditional retrieval systems. Image labels, as one of the most popular approaches for the semantic representation of images, can fully capture the representative information of images. To achieve the high performance of retrieval systems, the precise annotation for images becomes inevitable. However, as the massive number of images in the Internet, one cannot annotate all the images without a scalable and flexible (i.e., training-free) annotation method. In this paper, we particularly investigate the problem of accelerating sparse coding based scalable image annotation, whose off-the-shelf solvers are generally inefficient on large-scale dataset. By leveraging the prior that most reconstruction coefficients should be zero, we develop a general and efficient framework to derive an accurate solution to the large-scale sparse coding problem through solving a series of much smaller-scale subproblems. In this framework, an active variable set, which expands and shrinks iteratively, is maintained, with each snapshot of the active variable set corresponding to a subproblem. Meanwhile, the convergence of our proposed framework to global optimum is theoretically provable. To further accelerate the proposed framework, a sub-linear time complexity hashing strategy, e.g. Locality-Sensitive Hashing, is seamlessly integrated into our framework. Extensive empirical experiments on NUS-WIDE and IMAGENET datasets demonstrate that the orders-of-magnitude acceleration is achieved by the proposed framework for large-scale image annotation, along with zero/negligible accuracy loss for the cases without/with hashing speed-up, compared to the expensive off-the-shelf solvers.