Contextual Priming for Object Detection
International Journal of Computer Vision
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Structural Context for Object Categorization
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Independent shape component-based human activity recognition via Hidden Markov Model
Applied Intelligence
Optimal dynamic decision network model for scientific inquiry learning environment
Applied Intelligence
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Semi-Markov conditional random fields for accelerometer-based activity recognition
Applied Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A flexible edge matching technique for object detection in dynamic environment
Applied Intelligence
A target-based color space for sea target detection
Applied Intelligence
Unsupervised Learning of Categorical Segments in Image Collections
IEEE Transactions on Pattern Analysis and Machine Intelligence
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Image retrieval based on augmented relational graph representation
Applied Intelligence
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Annotating image regions with keywords has received increasing attention in the computer vision community in recent years. Recent studies have shown that graphical modeling techniques, such as Conditional Random Fields (CRF), greatly improves the accuracy of image annotation by utilizing contextual information among image regions. However, training and predicting with the high-order CRF is computational expensive so that only adjacent regions can be utilized to build its graph structure. In this paper, we develop a light-weight classification model, Approximated Supporting Region Graph (ASRG), in order to handle more relevant regions efficiently, with which a large number of supporting regions are selected and their features are utilized to represent the contextual information in the training and prediction for each image region. Experimental results show that our model is much more computational efficient and achieves competitive performance comparing with CRF and other state-of-art methods.