Making feature vector from Gabor filters for classification

Question

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

Answer 1

In general, there is nothing to think about - simple neural network requires one dimensional feature vector and does not care about the ordering, so you can simply concatenate any number of feature vectors into one (and even do it in random order - it does not matter). In particular if you have same feature matrices you also concatenate each of its row to create a vectorized format.

The only exception is when your data actually has some underlying geometrical dependicies, for example - matrix is actualy a pixels matrix. In such case architectures like PyraNet, Convolutional Neural Networks and others, which apply some kind of receptive fields based on this 2d structure - should be better. But those implementations simply accept 2d feature vector as an input.