Applying Gabor features for vehicle classification
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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
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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Greg Heath
2013-11-17
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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