International Journal of Computer Vision
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
On Image Classification: City vs. Landscape
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Scene categorization via contextual visual words
Pattern Recognition
A fast dual method for HIK SVM learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Computers and Electrical Engineering
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using Gabor-based direct linear discriminant analysis and support vector machine
Computers and Electrical Engineering
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Local and global features are considerably important features in computer vision and play an important role in scene categorization task. In this paper, an integrated feature description for scene categorization is constructed. First, we extract a type of extended contextual features for scene images that contain the local gradient information and more comprehensive local structural information. Mapping the local features by using improved LLC (Local-constrained Linear Coding) scheme to form the original image representation; Secondly, a set of global features named 'gist' are extracted that provide a statistical summary of the spatial layout properties of the scene; Then, the contextual features and 'gist' features are weighted combined based on their contribution for the integrated feature description, and each image is represented by using LLC scheme. Finally, we perform the scene categorization by libSVM with the HIK (Histogram Intersection Kernel) function. The proposed method achieves a satisfactory average accuracy rate 87.60% on a set of 15-scene categories.