Spatial Statistics of Visual Keypoints for Texture Recognition

In this paper, we propose a new descriptor of texture images based on the characterization of the spatial patterns of image keypoints. 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.