Deep Learning Guided SVM for Video Classification


Deep Learning Guided SVM for Video Classification – We present an algorithm that can extract 3D images based on depth maps, such that the pixel classifier can more accurately detect the full image. In this paper, we provide a practical solution to improve the performance of depth maps over existing state-of-the-art methods. Our deep method builds on a state-of-the-art deep convolutional neural network and a depth map projection model. The convolutional layer outputs a set of depth maps projected over the input image to produce the 3D object of the target object. In this way, the training data from a depth map is converted into the depth map projections. With our deep convolutional network, we can effectively use convolutional activations to capture the full depth map. Experiments are performed on various challenging image classification datasets and the proposed deep method outperforms previous state-of-the-art techniques on various objective functions.

Our goal is to learn continuous representations of input vectors with a novel nonparametric representation, in particular, the sparsity-inducing distribution of the sparse coefficients. By using a regularized kernel model as the input, we show that a sparsity-inducing distribution can significantly improve the performance of our method. We also demonstrate that both distributions are effectively trained on images with dense residual images (via a novel sparse coding model).

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Deep Learning Guided SVM for Video Classification

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  • Stochastic Optimization for Deep Neural Networks

    Stochastic Nonparametric Learning via Sparse CodingOur goal is to learn continuous representations of input vectors with a novel nonparametric representation, in particular, the sparsity-inducing distribution of the sparse coefficients. By using a regularized kernel model as the input, we show that a sparsity-inducing distribution can significantly improve the performance of our method. We also demonstrate that both distributions are effectively trained on images with dense residual images (via a novel sparse coding model).


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