人工智能图像识别代理通过可扩展的神经网络

R. H. P. Ebenezer, P. Manoj, H. R. Joseph, J. Visumathi
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引用次数: 0

摘要

到目前为止,在计算机科学的各个领域中已经提出了有效的图像识别和检索的无界解决方案。这些解决方案包括原始和复杂的方法,以最大限度地减少整个图像所需的输入,从而形成最准确的大图。提出的图像识别代理通过应用可扩展的神经网络来完成其任务。网络的每个神经元聚焦于作为一个片段的图像子集。神经元相对于动态知识库找到与获得的主题最接近的匹配。神经元网络集成它们的决策并提供识别的图像陈述。提供给神经网络的反馈增强了突触的强度,从而进一步提高了图像识别的性能和准确性。该系统采用了辅助监督学习和强化学习的概念,充分利用了人工神经网络的异步和并行计算能力。
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AI image recognizing agent through a scalable neural network
As of now, unbounded solutions for efficient image recognition and retrieval have been proposed within various fields of computer science. Such solutions include both primitive and complex approaches to minimize the input required from the whole of the image to form the most accurate idea of the big picture. The proposed image recognizing agent accomplishes its task though the application of a scalable neural network. Each neuron of the network focuses on a subset of the image taken as a segment. The neuron finds the closest match of the obtained subject with respect to the dynamic knowledge base. The network of neurons ensembles their decisions and provide the recognized image statement. The feedback provided to the network develops the synaptic strengths to further develop the image recognizing ability in terms of performance and accuracy. The system uses the concept of both assisted supervised learning and reinforced learning, and exploits the asynchronous and parallel computing ability of artificial neural networks.
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