SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
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
Contextual Priming for Object Detection
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Modeling Scenes with Local Descriptors and Latent Aspects
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
Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Content-Based Hierarchical Classification of Vacation Images
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Hi-index | 0.00 |
In this paper, we propose a novel scene categorization method based on contextual visual words. In this method, we extend the traditional `bags of visual words' model by introducing contextual information from the coarser scale level and neighbor regions to the local region of interest. The proposed method is evaluated over two scene classification datasets of 6,447 images altogether, with 8 and 13 scene categories respectively using 10-fold cross-validation. The experimental results show that the proposed method achieves 90.30% and 87.63% recognition success for Dataset 1 and 2 respectively, which significantly outperforms previous methods based on the visual words that represent the local information in a statistical manner. Furthermore, the proposed method also outperforms the spatial pyramid matching based scene categorization method, one of the scene categorization methods which achieved the best performance on these two datasets reported in previous literatures.