基于多层次神经网络的图像特征提取与分析算法

Erhui Xi
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引用次数: 2

摘要

本文研究了基于多级神经网络的图像特征提取与分析算法。作为机器学习的一个研究方向,深度学习方法受到了广泛关注。该方法通过结合底层特征来发现数据的不同特征表示,从而获得更抽象有效的高层语义信息。本研究旨在设计基于多层神经网络的模型,并实现特征提取管道。深度学习的核心是特征学习,其目的是通过分层网络获得分层特征信息。通过图像处理对该框架进行了验证。在公共数据库上对该算法进行了仿真,结果表明该算法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image feature extraction and analysis algorithm based on multi-level neural network
Image feature extraction and analysis algorithm based on multi-level neural network is studied in this paper. As a research direction of machine learning, deep learning method has been widely concerned. This method obtains more abstract and effective high-level semantic information by combining low-level features to discover different feature representations of data. This research work aims to design the model based on the multilevel neural network with the implementation of the feature extraction pipeline. The core of deep learning is feature learning, which aims to obtain the hierarchical feature information through a hierarchical network. The framework is validated through the image processing. The proposed algorithm is simulated on on the public database, and the result is efficient.
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