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Applying Gabor features for vehicle classification

Asked by Adil
on 14 Nov 2013
Latest activity Commented on by Adil
on 17 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

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. 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:

  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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1 Answer

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


  1 Comment

Yes I figured that out myself....the same day Thanks for your response by the way...

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