Asked by Adil
on 14 Nov 2013

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

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. squared Energy.**

**2. 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:

- 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?)
- 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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Answer by Greg Heath
on 17 Nov 2013

Accepted answer

If you have N I/O pairs of I-dimensional inputs and O-dimensional target outputs, the data matrices must have the sizes

[ I N ] = size(input)

[ O N ] = size(target)

Hope this helps.

**Thank you for formally accepting my answers**

Greg

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