Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Parsing: Unifying Segmentation, Detection, and Recognition
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
Estimating the Support of a High-Dimensional Distribution
Neural Computation
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
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Journal of Cognitive Neuroscience
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Unified Solution to Nonnegative Data Factorization Problems
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Proceedings of the ACM International Conference on Image and Video Retrieval
Projective nonnegative graph embedding
IEEE Transactions on Image Processing
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
This article investigates how to automatically complete the missing labels for the partially annotated images, without image segmentation. The label completion procedure is formulated as a nonnegative data factorization problem, to decompose the global image representations that are used for describing the entire images, for instance, various image feature descriptors, into their corresponding label representations, that are used for describing the local semantic regions within images. The solution provided in this work is motivated by following observations. First, label representations of the regions with the same label often share certain commonness, yet may be essentially different due to the large intraclass variations. Thus, each label or concept should be represented by using a subspace spanned by an ensemble of basis, instead of a single one, to characterize the intralabel diversities. Second, the subspaces for different labels are different from each other. Third, while two images are similar with each other, the corresponding label representations should be similar. We formulate this cross-image context as well as the given partial label annotations in the framework of nonnegative data factorization and then propose an efficient multiplicative nonnegative update rules to alternately optimize the subspaces and the reconstruction coefficients. We also provide the theoretic proof of algorithmic convergence and correctness. Extensive experiments over several challenging image datasets clearly demonstrate the effectiveness of our proposed solution in boosting the quality of image label completion and image annotation accuracy. Based on the same formulation, we further develop a label ranking algorithms, to refine the noised image labels without any manual supervision. We compare the proposed label ranking algorithm with the state-of-the-arts over the popular evaluation databases and achieve encouragingly improvements.