基于综合特征和多层感知器的图像分类

图像侦查已经成为军事侦查的主要方法之一，由于侦查图像数据量大，如何对前期图像正确分类，提高后期图像处理效率，成为研究的重点。不同目标类别的图像信息中所反映的特征不同，图像分类是指通过特征把不同类别的目标区分开。一种特征不能全面描述图像的信息，将纹理特征和灰度统计量特征组合为综合特征，多层感知器具有显著的学习和推理能力，可以解决复杂分类的问题，因此提出一种基于图像的综合特征和多层感知器相结合进行图像分类的方法。设计并实现了图像分类系统，使用标准图像库进行实验。首先提取图像的纹理特征和灰度特征，然后将选择的特征值组合成特征向量，进行归一化处理，作为多层感知器的输入，将预测的图像类别作为多层感知器的输出，从而得到分类结果。经过实例验证，分类准确率大于0.8，并将该分类系统应用在某型机试验结果评估系统，分类效果较好，可以为图像处理系统相关应用提供参考。

Image classification based on integrated features and multilayer perceptron

Image investigation has become one of the main methods of military investigation. Because of the large amount of data detected in the investigation, how to classify images correctly in the early stage and improve the efficiency of image processing in the later stage has become the focus of research. The features reflected in the image information of different target categories are different. Image classification refers to the distinction of different target categories by features. A feature can not describe the information of an image comprehensively. It combines texture features and gray statistics features into comprehensive features. The multi-layer perceptron has a remarkable ability of learning and reasoning, and it can solve the problem of complex classification. Therefore, a method of image classification based on the combination of image features and multi-layer perceptions is proposed. An image classification system is designed and implemented, using standard image library to conduct experiments. Firstly, the texture features and grayscale features are extracted, and then the selected eigenvalues are combined into eigenvectors for normalization, which is used as the input of the multi-layer perceptron, and the predicted image classes are used as the output of the multi-layer perceptron, and the result of classification is obtained. The classification accuracy is more than 0.8, and the classification system is applied to the test result evaluation system of a certain type of machine. The classification effect is good, which can provide reference for the related application of image processing system.