Fourier Transformations for Superpixel Segmentation in Natural Images

Fourier Transformations for Superpixel Segmentation in Natural Images – This paper presents a framework for automatic super-resolution for dense, high-resolution natural images by combining a semantic semantic super-resolution technique with deep learning. Our framework employs a deep neural network to learn a vectorially-decoded image descriptor. This descriptor is generated from the input image. The descriptor is transformed to a semantic representation (referred to as a semantic embeddings) and then a deep CNN, which integrates with a deep convolutional neural network (CNN), is trained to infer semantic embeddings. The CNN is evaluated on an image dataset of 3.0M images and the resulting super-resolution scheme is evaluated on an image dataset of 4.0M images. The proposed framework achieves state-of-the-art performance on the synthetic and on a fully-convolutional neural network (FCN) dataset with a computational cost of over $1^{-1}$ per image, which outperforms state-of-the-art super-resolution methods.

This paper presents Recurrent Neural Networks (RNNs) with a deep feature representation for image classification tasks. The deep features reflect the data representation in the form of a deep feature space, which have been integrated with a neural network to support the classification process. Since the deep features are similar in nature, an image classification model can be used to improve the classification accuracy. We propose a new deep recurrent network based on a recurrent neural network model with a deep feature representation, that learns features for deep features in the classification process. We train a neural network using a convolutional neural network to classify the data, and a convolutional neural network with a recurrent neural network to classify the images. With the deep feature representation, our model can significantly enhance performance in image classification tasks, by applying deep feature representation for classification. Experiments on both publicly available datasets, ImageNet, and ImageNet show that the proposed approach is competitive with existing methods in that it improves classification performance.

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Fourier Transformations for Superpixel Segmentation in Natural Images

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  • Boosted-Signal Deconvolutional Networks

    Improving Object Detection with Deep LearningThis paper presents Recurrent Neural Networks (RNNs) with a deep feature representation for image classification tasks. The deep features reflect the data representation in the form of a deep feature space, which have been integrated with a neural network to support the classification process. Since the deep features are similar in nature, an image classification model can be used to improve the classification accuracy. We propose a new deep recurrent network based on a recurrent neural network model with a deep feature representation, that learns features for deep features in the classification process. We train a neural network using a convolutional neural network to classify the data, and a convolutional neural network with a recurrent neural network to classify the images. With the deep feature representation, our model can significantly enhance performance in image classification tasks, by applying deep feature representation for classification. Experiments on both publicly available datasets, ImageNet, and ImageNet show that the proposed approach is competitive with existing methods in that it improves classification performance.


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