Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Semantic-Sensitive Classification for Large Image Libraries
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian network-based framework for semantic image understanding
Pattern Recognition
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 IV
Natural scene image modeling using color and texture visterms
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Scene Parsing Using Region-Based Generative Models
IEEE Transactions on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image classification for content-based indexing
IEEE Transactions on Image Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Region-based annotation of digital photographs
CCIW'11 Proceedings of the Third international conference on Computational color imaging
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This paper presents a novel approach based on contextual Bayesian networks (CBN) for natural scene modeling and classification. The structure of the CBN is derived based on domain knowledge, and parameters are learned from training images. For test images, the hybrid streams of semantic features of image content and spatial information are piped into the CBN-based inference engine, which is capable of incorporating domain knowledge as well as dealing with a number of input evidences, producing the category labels of the entire image. We demonstrate the promise of this approach for natural scene classification, comparing it with several state-of-art approaches.