1. Picture Comparison; now with shapes!
Slightly weak during MS1, only colour comparison
Several comparisons will be done
Turn picture into greyscale to do shape comparison
Use colour picture to do a colour histogram, and averaging/mixing
Histogram
Picture
Greyscale

2. Greyscale shapes Use greyscale picture and take contour
From the contour you can describe the shape
The first way is through approximation with central moments
Centeral Moments are position invariant
Grayscale
Contour

3. The Hero Ming-Kuei Hu
Central Moments describes the polygon through probability.
Hu-Moments are based on central moments
Hu-Moments are rotation and skewing invariant.
Using Hu-moments to describe the polygon means it doesn't matter how it's rotated, skewed, scaled or its position.
There is 7 Hu-moments and when you use them you get a single number for each of them, making them useful for searching.

4. The Hero Ming-Kuei Hu
Central Moments describes the polygon through probability.
Hu-Moments are based on central moments
Hu-Moments are rotation and skewing invariant.
Using Hu-moments to describe the polygon means it doesn't matter how it's rotated, skewed, scaled or its position.
There is 7 Hu-moments and when you use them you get a single number for each of them, making them useful for searching.

5. Fourier Descriptors
Second method to describe polygons is through Fourier descriptors
Describes the polygon with approximation using waves.
Each new wave makes the approximation more exact.
By using lower number of waves the approximation get rough, which at times is useful.

6. Colour matching
Use a histogram of colours in picture to see if they have similar set of colours
Mix together colours to bigger group to get rough placement of colours.(still dependant on rotation then)
Use a fully mixed picture(one colour) and histogram, for searching.

7. Searching Use a rough search that is low cost
Use more expensive search when they are accepted by low cost search.
Pre-process pictures and tag them for the low cost search.

8. OpenCV Contours Fourier
Centeral Moments(which can easily be used for Hu moments)
Histograms
Picture Handling
Pretty much everything I'd imagine needing

9. Papers!
Visual pattern recognition by moment invariants by Hu Ming-Kuei
Shape-based image retrieval using generic Fourier descriptor by Dengsheng Zhang & Guojun Lu
A comparative study of Fourier descriptors and Hu's seven moment invariants for image recognition by Qing Chen
Robust and Efficient Fourier–Mellin Transform Approximations for Gray-Level Image Reconstruction and Complete Invariant Description by Stéphane Derrodea & Faouzi Ghorbel