l2,1-norm regularized discriminative feature selection for unsupervised learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Proceedings of the 20th ACM international conference on Multimedia
Visual query attributes suggestion
Proceedings of the 20th ACM international conference on Multimedia
Robust cross-media transfer for visual event detection
Proceedings of the 20th ACM international conference on Multimedia
Local image tagging via graph regularized joint group sparsity
Pattern Recognition
Effective transfer tagging from image to video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Robust image annotation via simultaneous feature and sample outlier pursuit
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Online human gesture recognition from motion data streams
Proceedings of the 21st ACM international conference on Multimedia
Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Data centric research at the University of Queensland
ACM SIGMOD Record
Tag-Saliency: Combining bottom-up and top-down information for saliency detection
Computer Vision and Image Understanding
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Nowadays numerous social images have been emerging on the Web. How to precisely label these images is critical to image retrieval. However, traditional image-level tagging methods may become less effective because global image matching approaches can hardly cope with the diversity and arbitrariness of Web image content. This raises an urgent need for the fine-grained tagging schemes. In this work, we study how to establish mapping between tags and image regions, i.e. localize tags to image regions, so as to better depict and index the content of images. We propose the spatial group sparse coding (SGSC) by extending the robust encoding ability of group sparse coding with spatial correlations among training regions. We present spatial correlations in a two-dimensional image space and design group-specific spatial kernels to produce a more interpretable regularizer. Further we propose a joint version of the SGSC model which is able to simultaneously encode a group of intrinsically related regions within a test image. An effective algorithm is developed to optimize the objective function of the Joint SGSC. The tag localization task is conducted by propagating tags from sparsely selected groups of regions to the target regions according to the reconstruction coefficients. Extensive experiments on three public image datasets illustrate that our proposed models achieve great performance improvements over the state-of-the-art method in the tag localization task.