Fast Spatial-Aware Image Interpretation


Fast Spatial-Aware Image Interpretation – In previous approaches, image-guided semantic segmentation (e.g., FERET and RASCAL-2011) relies on the prior knowledge of the image. We propose a novel approach to extract relevant semantic information from the image. The objective function is to learn the information from the semantic domain, in terms of a latent variable, and to estimate the posterior of the semantic part of the image and present such posterior with high accuracy. We propose the idea of nonlinear estimation, in which the semantic part of each image is estimated from the input image into a latent space, where the probability of obtaining the data is proportional to the posterior. We validate the idea on various benchmarks, including MNIST, CIFAR-10, ImageNet, and SVHN, and show that our proposed method outperforms the rest of the proposed algorithms.

Most of the successful data mining models are based on the use of binary codes in the machine learning process. However, data mining models are often not binary and therefore require to update binary codes and thus fail to capture structural dependencies among binary codes. In this paper, we propose a novel data mining framework for feature selection of a data-rich structured data set. We formulate the problem as a sub-agent-based learning problem, and propose a data-based neural network learning technique called Deep Learning to extract features for a specific dataset. Our method is based on the use of random functions as parameter of learning of binary codes. The learned features are encoded and used to classify a dataset of users using different models. We validate the proposed method on a dataset of users’ ratings and find a very competitive performance compared to existing approaches (LSTM). Also, we illustrate the benefits of the proposed Deep Learning technique by demonstrating the performance of the learned feature extractors.

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Fast Spatial-Aware Image Interpretation

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  • Fast and Accurate Stochastic Variational Inference

    Deep learning-based registration for accurate sub-category analysis of dynamic point cloudsMost of the successful data mining models are based on the use of binary codes in the machine learning process. However, data mining models are often not binary and therefore require to update binary codes and thus fail to capture structural dependencies among binary codes. In this paper, we propose a novel data mining framework for feature selection of a data-rich structured data set. We formulate the problem as a sub-agent-based learning problem, and propose a data-based neural network learning technique called Deep Learning to extract features for a specific dataset. Our method is based on the use of random functions as parameter of learning of binary codes. The learned features are encoded and used to classify a dataset of users using different models. We validate the proposed method on a dataset of users’ ratings and find a very competitive performance compared to existing approaches (LSTM). Also, we illustrate the benefits of the proposed Deep Learning technique by demonstrating the performance of the learned feature extractors.


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