SIFT 和神经网络 Matlab
我的项目是识别古钱币。我使用 David Lowe 的 SIFT 算法来提取图像特征。
[siftImage, descriptors, locs] = sift(filteredImg);
现在我想将这些特征赋予神经网络来训练图像。
1）我应该向神经网络输入什么值作为输入？ （描述符向量或位置） 2）如何将它用于神经网络？
有人可以帮我吗？预先非常感谢。
My project is to recognize ancient coins. I have used David Lowe's SIFT algorithm to extract features of images.
[siftImage, descriptors, locs] = sift(filteredImg);
Now I want to give these features to a neural network for training images.
1) What value should I feed to Neural network as input? (descriptors vector or locs)
2) How can I use it for neural network?
Can someone please help me? Thanks a lot in advance.
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您需要手动对一些数据进行分类并对这些功能进行统计分析，以便了解哪些功能将为您提供最佳机会。
这可以从基本的直方图重叠（按类别划分的特征频率分布）到更复杂的多维聚类行为分析。
这将使您能够找到最适合神经网络用于分类的特征。
在分析数据之前，您不应该假设哪些功能最有用，因为您经常会发现意想不到的功能在新领域中提供有用的信息。
You need to manually categorise some of your data and perform a statistical analysis of the features, so understand which are going to give you the best chance.
This can go from a basic histogram overlap, of feature frequency distribution by category, to a more complex multi-dimensional cluster behaviour analysis.
This will enable you to find the features that seem be most suitable for the neural network to use for classification.
You should not make assumptions about which will be most useful before analysing the data, as you often find unexpected features give useful information in a new domain.