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My aim is to classify types of cars (Sedans,SUV,Hatchbacks) and earlier I was using corner features for classification but it didn't work out very well so now I am trying Gabor features.

code from here

Now the features are extracted and suppose when I give an image as input then for 5 scales and 8 orientations I get 2 [1x40] matrices.

1. 40 columns of squared Energy.

2. 40 colums of mean Amplitude.

Problem is I want to use these two matrices for classification and I have about 230 images of 3 classes (SUV,sedan,hatchback).

I do not know how to create a [N x 230] matrix which can be taken as vInputs by the neural netowrk in matlab.(where N be the total features of one image).

My question:

  1. How to create a one dimensional image vector from the 2 [1x40] matrices for one image.(should I append the mean Amplitude to square energy matrix to get a [1x80] matrix or something else?)

  2. Should I be using these gabor features for my purpose of classification in first place? if not then what?

Thanks in advance

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Well I figured it out myself that making a 1D vector of [1x80] by appending two [1x40] array worked and my classifier works very fine now

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