Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
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
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Putting Objects in Perspective
International Journal of Computer Vision
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Estimation of Object Position Based on Color and Shape Contextual Information
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Local normalized linear summation kernel for fast and robust recognition
Pattern Recognition
Scene classification based on multi-resolution orientation histogram of Gabor features
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Object classification using heterogeneous co-occurrence features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Image transform bootstrapping and its applications to semantic scene classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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This paper presents a scene classification method based on local co-occurrence in a KPCA space of local blob words. Scene classification based on local correlation of binarized projection lengths in subspaces obtained by Kernel Principal Component Analysis (KPCA) of visual words has been recently proposed, and its effectiveness has been demonstrated. However, the local correlation of two binary features (0 or 1) becomes 1 only when both features take a value of 1. The local correlation becomes 0 in all other cases ((0,1), (1,0) and (0,0)), which might lead to the loss of useful information for effective classification. In this study, all combinations of co-occurrence of binary features are used instead of local correlation. We conducted the experiments using a database containing 13 scene categories and found that the proposed method using local co-occurrence features achieves an accuracy of more than 84%, which is higher than the accuracy of conventional methods based on local correlation features.