Using Principal Component Analysis for corner feature extraction I am on a project of vehicle type Identification (MUV,jeep,Sedan,etc) from images and Here is what I have done up-to now on matlab.

I am working only on the sideview of images.


Input Image :  Masked image





Then I detected the corners easily




Now I got a matrix C with 40 corner coordinates thus I got a matrix of C=[40x2]

I converted it to a linear form using C=C(:); so I got C=[1x80] (as Neural Network destroys coordinates information itself) for 1 image
But I have about 9 image for each class(MUV,Jeep,Sedan) so I made a matrix using C matrix to form a new matrix say MAT=[9x80]

Now I have a corner feature of MAT=[9x80]

1. What Other features could I use or the corner feature would itself be fine enough.(may be SURF features)

2. The Input Format of NN is inMAT=[nxm] where n=number of features and m=total images and target Format is tgMAT=[cxm] where c=number of classes (MUV,Sedan,jeep) and 'm=total images'. Am I right here?
Neural networks can cope with not linearly separable classes, but they're not magic either. They still assume smooth decision boundaries, and I don't think your data would have those. 

And before you try to feed the same data to an SVM, RVM, random forest, or any other classifier: I don't think the corner coordinates are good features for any off-the-shelf classification algorithm. The typical input vector in machine learning is something like {age,weight,height,eye color}, where for every input vector, index 2 means "weight". In your input vector, index 2 can sometimes be a corner somewhere near the front wheel of the car, and in another case it's a corner near the roof. 

Have you tried simpler shape descriptors, like Fourier descriptors or maybe Hu moments?