A Comparative Study between Convolutional Neural Networks for Image Recognition, Predictive Modeling and Clustering


A Comparative Study between Convolutional Neural Networks for Image Recognition, Predictive Modeling and Clustering – As computer vision and image understanding becomes the focus of research, the research effort towards improving human perception of images can be enhanced by using deep neural networks (DNNs). DNNs are trained to capture a large part of the image features and generate a discriminant model for image classification, while relying only on semantic representations. In this paper we explore the use of deep neural networks for image classification using image-based learning of features and generate discriminant model for classification. We propose a classifier which combines features extracted from the input image with semantic representations extracted from the visual feature space. We also show that the discriminant model produced by these models are more accurate than a non-distributive model, demonstrating the utility of image classification to model human perception.

Our recently presented Deep-learning-based machine vision (Deep ML) method for the prediction of color and texture images has many of the characteristics of deep ML as well as of deep learning-based supervised learning. In this paper, we propose Deep ML – Deep Image Recurrent Machine (RD-RMS). Deep RL-M-S models are used as a model to generate realistic images of images which is a new feature of deep RL-M-S. We provide a comprehensive experimental evaluation test on both synthetic and real images using the MRC-100 Image Dataset. The experiments show the superiority of Deep RL-M-S over traditional methods in terms of accuracy and the transfer of pixel values to a more realistic image.

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A Comparative Study between Convolutional Neural Networks for Image Recognition, Predictive Modeling and Clustering

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  • Learning for Visual Control over Indoor Scenes

    Fast Bayesian Deep LearningOur recently presented Deep-learning-based machine vision (Deep ML) method for the prediction of color and texture images has many of the characteristics of deep ML as well as of deep learning-based supervised learning. In this paper, we propose Deep ML – Deep Image Recurrent Machine (RD-RMS). Deep RL-M-S models are used as a model to generate realistic images of images which is a new feature of deep RL-M-S. We provide a comprehensive experimental evaluation test on both synthetic and real images using the MRC-100 Image Dataset. The experiments show the superiority of Deep RL-M-S over traditional methods in terms of accuracy and the transfer of pixel values to a more realistic image.


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