RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian with Application to Material Recognition

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Authors Q. Wang, P. Li, W. Zuo and L. Zhang
Journal/Conference Name 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)
Paper Category
Paper Abstract Infinite dimensional covariance descriptors can provide richer and more discriminative information than their low dimensional counterparts. In this paper, we propose a novel image descriptor, namely, robust approximate infinite dimensional Gaussian (RAID-G). The challenges of RAID-G mainly lie on two aspects: (1) description of infinite dimensional Gaussian is difficult due to its non-linear Riemannian geometric structure and the infinite dimensional set-ting, hence effective approximation is necessary; (2) traditional maximum likelihood estimation (MLE) is not robust to high (even infinite) dimensional covariance matrix in Gaussian setting. To address these challenges, explicit feature mapping (EFM) is first introduced for effective ap-proximation of infinite dimensional Gaussian induced by additive kernel function, and then a new regularized MLE method based on von Neumann divergence is proposed for robust estimation of covariance matrix. The EFM and pro-posed regularized MLE allow a closed-form of RAID-G, which is very efficient and effective for high dimensional features. We extend RAID-G by using the outputs of deep convolutional neural networks as original features, and ap-ply it to material recognition. Our approach is evaluated on five material benchmarks and one fine-grained benchmark. It achieves 84.9curacy on FMD and 86.3curacyon UIUC material database, which are much higher than state-of-the-arts
Date of publication 2016
Code Programming Language MATLAB
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