Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Label to region by bi-layer sparsity priors
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Label-to-region with continuity-biased bi-layer sparsity priors
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Segmentation over detection by coupled global and local sparse representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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In this paper, we investigate how an unlabeled image corpus can facilitate the segmentation of any given image. A simple yet efficient multi-task joint sparse representation model is presented to augment the patch-pair similarities by harnessing the newly discovered patch-pair density priors. First, each image in over-segmented as a set of patches, and the adjacent patch-pair density priors, statistically calculated from the unlabeled image corpus, bring an intuitively explainable and informative observation that kindred patch-pairs generally have higher densities that inhomogeneous patch-pairs. Then for each adjacent patch-pair within the given image, high-density biased multi-task joint sparse reconstruction is pursued such that 1) both individual patches and patch-pair can be reconstructed with few patch-pairs from the unlabeled image corpus, and 2) the patch-pairs selected for reconstruction are high-density biased, namely, preferring patch-pairs belonging to the same semantic region. In this way, the overall reconstruction residue well conveys the discriminative information on whether these two patches belong to the same semantic region, and consequently the patch affinity matrix is augmented by reconstruction residues for all adjacent patch-pairs within the given image. The ultimate image segmentation is derived by employing the popular normalized cut approach over the augmented patch affinity matrix. Extensive image segmentation experiments over two public databases clearly demonstrate the superiority of the proposed solution over several state-of-the-art algorithms. Furthermore, the algorithmic practicality is well validated with comparison experiments on content-based image retrieval and multi-label image annotation performed over image segmentation outputs.