Towards Knowledge Based Image Retrieval


Towards Knowledge Based Image Retrieval – The goal is to use deep learning approach to automatically produce high quality images from a large database. But there are many reasons why it is not easy to do so. In this paper, we propose a novel approach to create a large-scale, low-cost image retrieval using multi-view semantic segmentation. Our architecture consists of two main components: (1) a robustly model-driven deep neural network (DRNN) module, (2) an image captioning approach that simultaneously learns a semantic model of the underlying image. Extensive experiments were conducted on the COCO dataset to show that our approach achieves state-of-the-art retrieval performance on a huge set of images.

Automatic diagnosis of diabetes mellitus (DM) is an important step towards the medical knowledge of the disease and the treatment of its symptoms. The use of automatic classification models such as the Haar-likelihood (HLD) classifier and the Monte Carlo (MC) classifier is a powerful tool for estimating the diagnosis and the parameters of the model. However, traditional approaches to automatic classification are not based on probabilistic models such as the Bayesian metric. In this paper, an automatic classification model such as the multivariate Multivariate (MM) model is presented in this paper. The MM classifier uses the distribution over the data to classify the parameters of the model. In addition, to analyze the relationship between the parameters of the MM model and the classifier, two methods are proposed to calculate the parameters of the model. In the MM model, two classes of parameters are computed based on the parameters of the model.

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Towards Knowledge Based Image Retrieval

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  • Mining Wikipedia Articles by Subject Headings and Video Summaries

    Towards Deep Learning Models for Electronic Health Records: A Comprehensive and Interpretable StudyAutomatic diagnosis of diabetes mellitus (DM) is an important step towards the medical knowledge of the disease and the treatment of its symptoms. The use of automatic classification models such as the Haar-likelihood (HLD) classifier and the Monte Carlo (MC) classifier is a powerful tool for estimating the diagnosis and the parameters of the model. However, traditional approaches to automatic classification are not based on probabilistic models such as the Bayesian metric. In this paper, an automatic classification model such as the multivariate Multivariate (MM) model is presented in this paper. The MM classifier uses the distribution over the data to classify the parameters of the model. In addition, to analyze the relationship between the parameters of the MM model and the classifier, two methods are proposed to calculate the parameters of the model. In the MM model, two classes of parameters are computed based on the parameters of the model.


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