Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
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
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Discriminative Object Class Models of Appearance and Shape by Correlatons
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Description of interest regions with local binary patterns
Pattern Recognition
Viewpoint Invariant Texture Description Using Fractal Analysis
International Journal of Computer Vision
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Feature Extraction by a Multimarked Point Process
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Detecting parametric objects in large scenes by Monte Carlo sampling
International Journal of Computer Vision
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In this paper, we propose a new descriptor of texture images based on the characterization of the spatial patterns of image key-points. Regarding the set of visual keypoints of a given texture sample as the realization of marked point process, we define texture features from multivariate spatial statistics. Our approach initially relies on the construction of a codebook of the visual signatures of the keypoints. Here these visual signatures are given by SIFT feature vectors and the codebooks are issued from a hierarchical clustering algorithm suitable for processing large high-dimensional dataset. The texture descriptor is formed by cooccurrence statistics of neighboring keypoint pairs for different neighborhood radii. The proposed descriptor inherits the invariance properties of the SIFT w.r.t. contrast change and geometric image transformation (rotation, scaling). An application to texture recognition using the discriminative classifiers, namely: k-NN, SVM and random forest, is considered and a quantitative evaluation is reported for two case-studies: UIUC texture database and real sonar textures. The proposed approach favourably compares to previous work. We further discuss the properties of the proposed descriptor, including dimensionality aspects.