I am doing a project on vehicle type classification with Neural Networks( classification basis is => sedan,pick up,hatchback,etc type vehicles.)
I am doing image processing for the first time and I have detected about 40 corners using Harris Edge Detection and thus I got a matrix A[40x2].
I am using only this feature as for classification.
Now I want to know how can I use PCA to extract features from it.I know what PCA is and what pca(A) or princomp(A)in matlab will return but I dont get how to use the output of pca function as a feature matrix.
1.Does a feature matrix need to be 1-D array.
2.Should I use principle components array which is the 2nd matrix returned by pca function as a feature matrix (its a 2-D matrix)
3.How can I train Neural Network for 3 classes(hatchback,sedan and pickup).
4.Lastly suppose I have N images for each class to train so do I need to train on each image individually or do I have to create a feature matrix that has a extra dimension = N.
ANSWER
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% Extracting Corner Features with PCA and feeding it to neural network% Asked by Adil about 2 hours ago% I am doing a project on vehicle type classification with Neural Networks( % classification basis is => sedan,pick up,hatchback,etc type vehicles.) % % I am doing image processing for the first time and I have detected about % 40 corners using Harris Edge Detection and thus I got a matrix A[40x2]. A = A(:); % Convert to an 80 dim column%I am using only this feature as for classification.%Now I want to know how can I use PCA to extract features from it.I know what PCA is and what %pca(A) or princomp(A)in matlab will return but I dont get how to use the output of pca function %as a feature matrix. You are on the wrong track 1. Use PCA on all of the data to determine the ranking of orthogonal directions in which the data has the most spread. This is very useful for regression/curve-fitting but not necessarily for classification/pattern-recognition where the importance is on ranking directions of class separation, not total mixture spread.2. The function PLS is the feature ranking function which is more appropo. I am struggling with it's use because I have not found an explanation which I fully understand. So, I do use PCA even though I know better.
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
% Extracting Corner Features with PCA and feeding it to neural network
% Asked by Adil about 2 hours ago
% I am doing a project on vehicle type classification with Neural Networks( % classification basis is => sedan,pick up,hatchback,etc type vehicles.) % % I am doing image processing for the first time and I have detected about % 40 corners using Harris Edge Detection and thus I got a matrix A[40x2].
A = A(:); % Convert to an 80 dim column
%I am using only this feature as for classification.
%Now I want to know how can I use PCA to extract features from it.I know what PCA is and what %pca(A) or princomp(A)in matlab will return but I dont get how to use the output of pca function %as a feature matrix.
You are on the wrong track
1. Use PCA on all of the data to determine the ranking of orthogonal directions in which the data has the most spread. This is very useful for regression/curve-fitting but not necessarily for classification/pattern-recognition where the importance is on ranking directions of class separation, not total mixture spread.
2. The function PLS is the feature ranking function which is more appropo. I am struggling with it's use because I have not found an explanation which I fully understand. So, I do use PCA even though I know better.
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