In this paper, we address invariant keypoint-based texture characterization and recognition. Viewing keypoint sets associated with visual textures as realizations of point processes, we investigate probabilistic texture models from multivariate log-Gaussian Cox processes. These models are parameterized by the covariance structure of the spatial patterns. Their implementation initially rely on the construction of a codebook of the visual signatures of keypoints. We discuss invariance properties of the proposed models for texture recognition applications and report a quantitative evaluation for three texture datasets, namely: UIUC, KTH-TIPs and Brodatz. These experiments include a comparison of the performance reached using different methods for keypoint detection and characterization and demonstrate the relevance of the proposed models w.r.t. state-of-the-art methods. We further discuss the main contribution of proposed approach, including the key features of a statistical model and complexity aspects.
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