With the rapid development of Internet information technology, image data is explosively growing. How to quickly and effectively acquire and manage these image information has become a research hotspot in the computer field. It is precisely because images can display information intuitively, and people can get the information they need directly from images. Therefore, people are more accustomed to using images as a more commonly used medium to replace large amounts of text to convey information, which is also the main reason for the rapid growth of image information. In the face of such vast image data, if it can not be used reasonably and fully, it is obviously a great waste of resources. If we want to make full use of these resources, we need to organize and manage them effectively. The research of image classification involves many interdisciplinary disciplines such as mathematics, biology, medicine, pattern recognition, machine learning, artificial intelligence and so on. Its development has gone through many stages. Recently, the hot research on deep learning methods has brought new opportunities to image classification. In this paper, a meta-learning framework based on multi-intelligent nervous system is proposed for image classification and recognition. We integrate the multiple-capsule intelligent neural systems to construct the efficient model. The experimental results show that the proposed algorithm has higher image recognition rate.

随着Internet信息技术的飞速发展，图像数据呈爆炸性增长。如何快速有效地获取和管理这些图像信息已经成为计算机领域的研究热点。正是因为图像可以直观地显示信息，并且人们可以直接从图像中获得所需的信息。因此，人们越来越习惯于使用图像作为更常用的媒介来代替大量的文本来传递信息，这也是图像信息快速增长的主要原因。面对如此庞大的图像数据，如果不能合理，充分地利用它，显然是对资源的极大浪费。如果我们想充分利用这些资源，就需要有效地组织和管理它们。图像分类研究涉及数学，生物学，医学，模式识别，机器学习，人工智能等众多交叉学科。它的发展经历了许多阶段。近年来，关于深度学习方法的热门研究为图像分类带来了新的机遇。本文提出了一种基于多智能神经系统的元学习框架，用于图像的分类和识别。我们集成了多胶囊智能神经系统，以构建有效的模型。实验结果表明，该算法具有较高的图像识别率。深度学习方法的热门研究为图像分类带来了新的机遇。本文提出了一种基于多智能神经系统的元学习框架，用于图像的分类和识别。我们集成了多胶囊智能神经系统，以构建有效的模型。实验结果表明，该算法具有较高的图像识别率。深度学习方法的热门研究为图像分类带来了新的机遇。本文提出了一种基于多智能神经系统的元学习框架，用于图像的分类和识别。我们集成了多胶囊智能神经系统，以构建有效的模型。实验结果表明，该算法具有较高的图像识别率。