Content-Based Image Retrieval at the End of the Early Years
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
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Image annotation: which approach for realistic databases?
Proceedings of the 6th ACM international conference on Image and video retrieval
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Probabilistic linear discriminant analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Improving local descriptors by embedding global and local spatial information
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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Recently, generic image recognition techniques are widely studied for automatic image indexing. However, much of these works are computationally too heavy for practical large setup. Thus, it is very important to properly balance the trade-off between performance and computational cost for realizing scalability. In recent years, methods based on the bag-of-keypoints technique have been quite successful and are widely used. However, preprocessing cost for building visual words becomes immense in large scale datasets. On the other hand, methods based on global image features have been used for a long time. Because global image features can be extracted rapidly, it is relatively easy to use them with very large datasets. However, the performance of global feature methods is usually poor compared to bag-of-keypoints. In this paper, we propose a very simple but powerful scheme of boosting the performance of global image features, by densely sampling low-level statistics (mean and correlation) of local features. Also, we use a highly scalable learning and classification method which is substantially lighter than SVM. Our method achieved the performance comparable to state-of-the-art methods in spite of its remarkable simplicity.