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 | 1 | initial version |
I am not sure what's the sense of using LBP codes for the MNIST Dataset. It should be totally sufficient to apply a PCA directly. Also note, that mathematically a k-means method yields the same clusters as a PCA projection, there are papers about this (I am on my mobile phone, so I can't look up the title).
So there's also no real need for performing a k-means clustering on the projected samples. What I would try instead is to see, what clusters a Linear Discriminant Analysis yields. Then I would play around with OpenCV's awesome machine learning library and see how a Multi Layer Perceptron performs or how a SVM performs on the raw image data (with different kernels).
Regarding Local Binary Patterns, there's a much simpler (and tested!) implementation in the face recognition code I provide. The OpenCV implementation in cv::FaceRecognizer is the same. I am linking to the original project (libfacerec), because everything is in one file there:
If you need some ideas on how to work with OpenCV machine learning, you can have a look into my Guide to Machine Learning with OpenCV:
There are also tutorials on using a SVM in the official OpenCV documentation:
| | 2 | added citation |
I am not sure what's the sense of using LBP codes for the MNIST Dataset. It should be totally First try to apply a PCA directly, I think it's sufficient to apply a PCA directly. Also already. Then note, that mathematically a k-means method yields the same clusters centroids as a PCA projection, there are papers about this (I am on my mobile phone, so I can't look up the title).
So there's also no real need for to performing a k-means clustering on the projected samples. What I would try instead is to see, what clusters a Linear Discriminant Analysis yields. Then I would play around with OpenCV's awesome machine learning library and see how a Multi Layer Perceptron performs or how a SVM performs on the raw image data (with different kernels).
Regarding Local Binary Patterns, there's a much simpler (and tested!) implementation in the face recognition code I provide. The OpenCV implementation in cv::FaceRecognizer is the same. I am linking to the original project (libfacerec), because everything is in one file there:
If you need some ideas on how to work with OpenCV machine learning, you can have a look into my Guide to Machine Learning with OpenCV:
There are also tutorials on using a SVM in the official OpenCV documentation:
| | 3 | No.3 Revision |
First try to apply a PCA directly, I think it's sufficient already. Then note, that mathematically a k-means method yields the same centroids as a PCA projection, see:
So there's no need to performing a k-means clustering on the projected samples. What I would try instead is to see, what clusters a Linear Discriminant Analysis yields. A Linear Discriminant Analysis is available as cv::LDA in the contrib module of OpenCV:
Then I would play around with OpenCV's awesome machine learning library and see how a Multi Layer Perceptron performs or how a SVM performs on the image data (with different kernels).
Regarding Local Binary Patterns, there's a much simpler (and tested!) implementation in the face recognition code I provide. The OpenCV implementation in cv::FaceRecognizer is the same. I am linking to the original project (libfacerec), because everything is in one file there:
If you need some ideas on how to work with OpenCV machine learning, you can have a look into my Guide to Machine Learning with OpenCV:
There are also tutorials on using a SVM in the official OpenCV documentation:
| | 4 | No.4 Revision |
First try to apply a PCA directly, I think it's sufficient already. Then note, that mathematically a k-means method yields the same centroids as a PCA projection, see:
So there's no need to performing a k-means clustering on the projected samples. What I would try instead is to see, what clusters a Linear Discriminant Analysis yields. A Linear Discriminant Analysis is available as cv::LDA in the contrib module of OpenCV:
Then I would play around with OpenCV's awesome machine learning library and see how a Multi Layer Perceptron performs or how a SVM performs on the image data (with different kernels).
Regarding Local Binary Patterns, there's a much simpler (and tested!) implementation in the face recognition code I provide. The OpenCV implementation in cv::FaceRecognizer is the same. I am linking to the original project (libfacerec), because everything is in one file there:
If you need some ideas on how to work with OpenCV machine learning, you can have a look into my Guide to Machine Learning with OpenCV:
There are also tutorials on using a SVM in the official OpenCV documentation:
| | 5 | No.5 Revision |
First try to apply a PCA directly, I think it's sufficient already. Then note, that mathematically a k-means method yields the same centroids as a PCA projection, see:
So there's no need to performing perform a k-means clustering on the projected samples. What I would try instead is to see, what clusters a Linear Discriminant Analysis yields. A Linear Discriminant Analysis is available as cv::LDA in the contrib module of OpenCV:
Then I would play around with OpenCV's awesome machine learning library and see how a Multi Layer Perceptron performs or how a SVM performs on the image data (with different kernels).
Regarding Local Binary Patterns, there's a much simpler (and tested!) implementation in the face recognition code I provide. The OpenCV implementation in cv::FaceRecognizer is the same. I am linking to the original project (libfacerec), because everything is in one file there:
If you need some ideas on how to work with OpenCV machine learning, you can have a look into my Guide to Machine Learning with OpenCV:
There are also tutorials on using a SVM in the official OpenCV documentation:
