搜索资源列表
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C++编写的图像分类程序，实现图像的分类。-C++ write image classification procedures, classification of images.
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利用最小错分概率贝叶斯分类器进行图像分类的基本方法，将模式识别方法与图像处理技术相结合-The basic method of image classification using the minimum error probability Bias classifier, the pattern recognition method and image processing technology
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利用K均值聚类算法进行图像分类的基本方法，将模式识别方法与图像处理技术相结合，-Using K means clustering algorithm to carry out the basic method of image classification, the pattern recognition method and image processing technology,
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将模式识别方法与图像处理技术相结合，利用最小错分概率贝叶斯分类器进行图像分类-The pattern recognition method and image processing technology are combined, using the minimum error probability Bias classifier for image classification
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在图像精度评价中，主要用于比较分类结果和实际测得值，可以把分类结果的精度显示在一个混淆矩阵里面。混淆矩阵是通过将每个实测像元的位置和分类与分类图像中的相应位置和分类像比较计算的。-In the image to uate the accuracy, mainly for comparison of classification results and the actual measured value, the accuracy of the classification results ar
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利用最大似然法，通过感兴趣区域对遥感图像进行分类-Using the maximum likelihood method, by region of interest on remote sensing image classification
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kmeans用于图像分类，在LLC方法中-using kmeans for image classification in LLC
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用极限稀疏矩阵对图像分类，该方法快速简单，代码少，效果好-An ultimate sparse matrix for image classification, the process quick and easy, less code, the effect is good
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Image classification using RBFSVM and Manhattan distance. It detects face and eye. Face detection using Viola jones.
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PCANet_train, 基于主成分分析是卷积神经网络的训练过程。-A simple deep learning baseline for image classification
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PCANet_FeaExt，基于主成分分析的卷积神经网络的特征提取过程。-A simple deep learning baseline for image classification
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PCANet_FilterBank，基于主成分分析的卷积神经网络,其中主成分分析得到的主元作为网络映射的滤波器。-PCA was used as the filter in PCANets. It is really useful for image classification.
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用于主成分图像svm分类，简单，有很好的程序，适合初学者(SVM for principal component image classification, simple, there are very good procedures for beginners)
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pso svm classification
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利用能量最小法中的图割法，解决图像分类问题，最大流=最小割(The minimum method of energy in the map cut method to solve the problem of image classification)
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利用Google inception V3 实现迁移学习 进行图像分类(Using Google, inception, V3 to achieve migration learning, image classification)
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基于底层特征和SVM的图像分类image classification based on the underlying characteristics and the SVM(image classification based on the underlying characteristics and the SVM)
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特征提取，提取大量特征用于图像的分类识别(Extracting a large number of features for image classification)
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利用Kmeans对高光谱图像分类,很好的程序(hyperspectral image classification using kmeans method)
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使用HOG提取特征，SVM进行图像分类，可以进行两种以上分类(Using HOG to extract features and SVM for image classification)
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