Deep Learning for Real Detection with Composed-Seq Images


Deep Learning for Real Detection with Composed-Seq Images – Image data have been a major source of error during the past decades. The primary focus of this paper is to develop a robust and practical framework for image retrieval (i.e. the extraction of images from social media). The data collected from social media content of the internet-based web enables to extract relevant features from the images, such as semantic, visual, contextual, language, and textual labels. We show that, although natural language processing (NN) approaches can extract these features without using images, it is not practical for using social networks for this purpose. To address the problem, we propose a deep convolutional neural network (CNN) with feature extraction algorithms, which significantly outperforms the state-of-the-art. This is in accord with the proposed training paradigm, which combines the best techniques from CNNs with image extraction. We illustrate the benefits of the proposed methodology using both synthetic and real data sets, showing that for a given dataset, learning the features is far from the best solution.

To obtain an informed opinion on the proposed method, the authors have designed two software projects: The Inter-Agency Biometric Machine Learning, which implements the new algorithms and is based on a prototype. The Machine Learning project focuses on the identification of the object, the data collection and the user experience. The main contributions of the Machine Learning project involved: (1) developing an online algorithm for solving the object recognition problem, (2) building an end-to-end solution for the application, (3) developing the algorithm and making use of the generated samples.

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Deep Learning for Real Detection with Composed-Seq Images

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    The Interactive Biometric PlatformTo obtain an informed opinion on the proposed method, the authors have designed two software projects: The Inter-Agency Biometric Machine Learning, which implements the new algorithms and is based on a prototype. The Machine Learning project focuses on the identification of the object, the data collection and the user experience. The main contributions of the Machine Learning project involved: (1) developing an online algorithm for solving the object recognition problem, (2) building an end-to-end solution for the application, (3) developing the algorithm and making use of the generated samples.


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