The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Active learning with strong and weak views: a case study on wrapper induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
Human action recognition via multi-view learning
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
| Hi-index | 0.00 |
Inspired by the idea of multi-view, we proposed an image segmentation algorithm using co-EM strategy in this paper. Image data are modeled using Gaussian Mixture Model (GMM), and two sets of features, i.e. two views, are employed using co-EM strategy instead of conventional single view based EM to estimate the parameters of GMM. Compared with the single view based GMM-EM methods, there are several advantages with the proposed segmentation method using co-EM strategy. First, imperfectness of single view can be compensated by the other view in the co-EM. Second, employing two views, co-EM strategy can offer more reliability to the segmentation results. Third, the drawback of local optimality for single view based EM can be overcome to some extent. Fourth, the convergence rate is improved. The average time is far less than single view based methods. We test the proposed method on large number of images with no specified contents. The experimental results verify the above advantages, and outperform the single view based GMM-EM segmentation methods.