Conference Paper

Filter Forests for Learning Data-Dependent Convolutional Kernels

DOI: 10.1109/CVPR.2014.221 Conference: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context. FF can be used for general signal restoration tasks that can be tackled via convolutional filter-ing, where it attempts to learn the optimal filtering kernels to be applied to each data point. The model can learn both the size of the kernel and its values, conditioned on the ob-servation and its spatial or temporal context. We show that FF compares favorably to both Markov random field based and recently proposed regression forest based approaches for labeling problems in terms of efficiency and accuracy. In particular, we demonstrate how FF can be used to learn optimal denoising filters for natural images as well as for other tasks such as depth image refinement, and 1D signal magnitude estimation. Numerous experiments and quanti-tative comparisons show that FFs achieve accuracy at par or superior to recent state of the art techniques, while being several orders of magnitude faster.

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Available from: Sean Ryan Fanello, May 27, 2014
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    ABSTRACT: Learning regressors from low-resolution patches to high-resolution patches has shown promising results for image super-resolution. We observe that some regressors are better at dealing with certain cases, and others with different cases. In this paper, we jointly learn a collection of regressors, which collectively yield the smallest super-resolving error for all training data. After training, each training sample is associated with a label to indicate its 'best' regressor, the one yielding the smallest error. During testing, our method bases on the concept of 'adaptive selection' to select the most appropriate regressor for each input patch. We assume that similar patches can be super-resolved by the same regressor and use a fast, approximate kNN approach to transfer the labels of training patches to test patches. The method is conceptually simple and computationally efficient, yet very effective. Experiments on four datasets show that our method outperforms competing methods.
    Full-text · Conference Paper · May 2015