Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Effective automatic image annotation via a coherent language model and active learning
Proceedings of the 12th annual ACM international conference on Multimedia
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - 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
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Group lasso with overlap and graph lasso
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Label to region by bi-layer sparsity priors
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
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Leveraging loosely-tagged images and inter-object correlations for tag recommendation
Proceedings of the international conference on Multimedia
Image segmentation with patch-pair density priors
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
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
In this work, we investigate how to reassign the fully annotated labels at image level to those contextually derived semantic regions, namely Label-to-Region (L2R), in a collective manner. Given a set of input images with label annotations, the basic idea of our approach to L2R is to first discover the patch correspondence across images, and then propagate the common labels shared in image pairs to these correlated patches. Specially, our approach consists of following aspects. First, each of the input images is encoded as a Bag-of-Hierarchical-Patch (BOP) for capturing the rich cues at variant scales, and the individual patches are expressed by patch-level feature descriptors. Second, we present a sparse representation formulation for discovering how well an image or a semantic region can be robustly reconstructed by all the other image patches from the input image set. The underlying philosophy of our formulation is that an image region can be sparsely reconstructed with the image patches belonging to the other images with common labels, while the robustness in label propagation across images requires that these selected patches come from very few images. This preference of being sparse at both patch and image level is named bi-layer sparsity prior. Meanwhile, we enforce the preference of choosing larger-size patches in reconstruction, referred to as continuity-biased prior in this work, which may further enhance the reliability of L2R assignment. Finally, we harness the reconstruction coefficients to propagate the image labels to the matched patches, and fuse the propagation results over all patches to finalize the L2R task. As a by-product, the proposed continuity-biased bi-layer sparse representation formulation can be naturally applied to perform image annotation on new testing images. Extensive experiments on three public image datasets clearly demonstrate the effectiveness of our proposed framework in both L2R assignment and image annotation.