A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Object Categorization by Learned Universal Visual Dictionary
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
International Journal of Computer Vision
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Representing shape with a spatial pyramid kernel
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
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
Localizing Objects with Smart Dictionaries
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Latent mixture vocabularies for object categorization and segmentation
Image and Vision Computing
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning color names for real-world applications
IEEE Transactions on Image Processing
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-to-class distance metric learning for image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A fast dual method for HIK SVM learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Efficient highly over-complete sparse coding using a mixture model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Hierarchical Correlation of Multi-Scale Spatial Pyramid for Similar Mammogram Retrieval
International Journal of Digital Library Systems
Heterogeneous bag-of-features for object/scene recognition
Applied Soft Computing
Fusing color and shape for bag-of-words based object recognition
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
Coloring Action Recognition in Still Images
International Journal of Computer Vision
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Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.