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Overview of FindPic
What it does
How it works (from a user standpoint)
Modes
– grayscale– quantized color– texture for both
Technique: SOM Overview
Gather image data (feature vectors)
Create matrix (with same size vectors)
For each image find best match in matrix
Change matrix node and surrounding nodes
Reduce neighborhood size and change factor
Re-run for a number of epochs
Setting up the Map
2D Array of matrix nodes
Same feature vectors
Initialized to random values
Mapping begins
Finding the Best Match
Euclidean distance between the image (input) vectors and the matrix vectors
D mV 0 iV 0 2 mV 1 iV 1 2 ... mV n 1 iV n 1 2
Making Changes
After best match is found change the matrix node is made to be more like the input node
e.g. matrixVector[0] = 50, imageVector[0] = 30, cf = 0.9
matrixVector[0] = 50 – 0.9(50 – 30) = 32
e.g. matrixVector[0] = 30, imageVector[0] = 50, cf = 0.9
matrixVector[0] = 30 – 0.9(30 – 50) = 48
matrixVector i matrixVector i cf matrixVector i imageVector i
Change Over Time
Inverse parabola determines neighborhood and change factor
yx2
Mt Where M=matrix dimension and t = time
Sample Neighborhood
Best Matching unit is set to position (1,1) with t = 1
0.8 0.9 0.8 0.5 0.0 -0.7 0.9 1.0 0.9 0.6 0.1 -0.6 0.8 0.9 0.8 0.5 0.0 -0.7 0.5 0.6 0.5 0.2 -0.3 -1.0 0.0 0.1 0.0 -0.3 -0.8 -1.5-0.7 -0.6 -0.7 -1.0 -1.5 -2.2
Sample Neighborhood
Best Matching unit is set to position (1,1) with t = 0.3
0.1 0.2 0.1 -0.2 -0.7 -1.4 0.2 0.3 0.2 -0.1 -0.6 -1.3 0.1 0.2 0.1 -0.2 -0.7 -1.4-0.2 -0.1 -0.2 -0.5 -1.0 -1.7-0.7 -0.6 -0.7 -1.0 -1.5 -2.2-1.4 -1.3 -1.4 -1.7 -2.2 -2.9
End Results
Images with similar feature vectors should be grouped into the same matrix cells.
Surrounding cells should also contain images that are similar.