The Random Subspace Method for Constructing Decision Forests
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
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Deformation Invariant Image Matching
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
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Description of interest regions with local binary patterns
Pattern Recognition
MMEDIA '10 Proceedings of the 2010 Second International Conferences on Advances in Multimedia
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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
Integrated image representation based natural scene classification
Expert Systems with Applications: An International Journal
Building global image features for scene recognition
Pattern Recognition
Improvements in image categorization using codebook ensembles
Image and Vision Computing
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Discriminative compact pyramids for object and scene recognition
Pattern Recognition
The combination of differential evolution and color attention for object recognition
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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In this work we propose a method for object recognition based on a random selection of interest regions, heterogeneous set of texture descriptors and a bag-of-features approach based on several k-means clustering runs for obtaining different codebooks. The proposed system is not based on complex region detection as SIFT but on a simple exhaustive extraction of sub-windows of a given image. In the classification step an ensemble of random subspace of support vector machine (SVM) is used. The use of random subspace ensemble coupled to the principal component analysis for reducing the dimensionality of the descriptors permits to reduce the curse of dimensionality problem. In the experimental section we show that the combination of classifiers trained using different descriptors permits a consistent improvement of the performance of the stand alone approaches. The proposed system has been tested on four datasets: in the VOC2006 dataset, in a wide-used scene recognition dataset, in the well-known Caltech-256 Object Category Dataset and in a landmark dataset, obtaining remarkable results with respect to other state-of-the-art approaches. The MATLAB code of our system is publicly available.