Is there a better Fourier Transform-based algorithm to use in Image recognition

Question

I have been trying to use the Fourier transform in recognizing images of the same size 200x400.

I have tried many different ways to do that such as:

1. Performing the Fourier on the full image and then try to match it with the ones in the database. the accuracy rate was very bad.
2. Performing the Fourier on the full image and then take only the sub-matrix contained in centre of the image and then match it with the ones in the database. the accuracy rate was also very bad.
3. Performing the Fourier on the full image and then take only the horizontal and vertical vector that intersect in the centre of the image, and then match it with the ones in the database. also, the accuracy rate was very bad.

Is there any better algorithm that might help me solve this problem?

Thank you very much

Answer 1

The Fourier Transform is good for picking up some similarities between images:

• Shifts in $x$ and $y$ are just phase changes in the Fourier domain.
• Rotations in the $x-y$ plane cause rotations in the Fourier domain.

So, if this is what you are trying to achieve, then it makes sense.

See this paper for a performance analysis of doing this using the following algorithm

Answer 2

Matching images is very common function in image processing, and there is a great deal of literature available on the subject. Cross-correlation via template (or pattern) matching is one of many ways of accomplishing it. Download the following tutorial to see an explanation and C++ example:

http://fftguru.com/fftguru.com.tutorial3.pdf

Note that, in the code, the 2D inverse FFT is accomplished by reversing the FFT arguments (ie: if fft_8(r1,i1) is the forward 2D transform, then fft_8(i1,r1) is the inverse 2D transform - the fft routine must, of course, be for complex inputs/outputs).

There are many other image processing tutorials available, some of which use different methods.