Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth 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
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Large scale manifold transduction
Proceedings of the 25th international conference on Machine learning
Learning to reduce the semantic gap in web image retrieval and annotation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Hidden-concept driven image decomposition towards semi-supervised multi-label image annotation
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Efficient semi-supervised learning on locally informative multiple graphs
Pattern Recognition
Manifold-ranking based retrieval using k-regular nearest neighbor graph
Pattern Recognition
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With fast growing number of images on photo-sharing websites such as Flickr and Picasa, it is in urgent need to develop scalable multi-label propagation algorithms for image indexing, management and retrieval. It has been well acknowledged that analysis in semantic region level may greatly improve image annotation performance compared to that in the holistic image level. However, region level approach increases the data scale to several orders of magnitude and proposes new challenges to most existing algorithms. In this work, we present a novel framework to effectively compute pairwise image similarity by accumulating the information of semantic image regions. Firstly, each image is encoded as Bag-of-Regions based on multiple image segmentations. Secondly, all image regions are separated into buckets with efficient locality-sensitive hashing (LSH) method, which guarantees high collision probabilities for similar regions. The k-nearest neighbors of each image and the corresponding similarities can be efficiently approximated with these indexed patches. Lastly, the sparse and region-aware image similarity matrix is fed into the multi-label extension of the entropic graph regularized semi-supervised learning algorithm [1]. In combination they naturally yield the capability of handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets validate the effectiveness and efficiency of our proposed framework for region-aware and scalable multi-label propagation.