ImLab Scilab Function
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imkohonentrain - Kohonen networks method, training phase: second step of image segmentation using Kohonen networks clustering method.
Calling Sequence
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[prototype] = imkohonentrain(learningdata,classnb[,iterationnb])
Parameters
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learningdata
: set of pixels attributes values. This data is obtained with 'imlearningdata' Imlab function.
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classnb
: number of classes (or regions) that must be created during the segmentation.
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iterationnb
: minimum number of iterations that must be executed. Default value used is 1. The training phase stops after at least 'iterationnb' iterations and when all the objects have been presented to the network at least twice.
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prototype
: representative values of the classes. If learningdata parameter is a 2D matrix, the result prototype is also a 2D matrix where each line corresponds to an attribute in learningdata and each column corresponds to a class. If learningdata parameter is a 3D hypermatrix, the result prototype is also a 3D hypermatrix where each plan corresponds to a pixel component in learningdata.
Description
Thanks to learning data and following Kohonen networks clustering method, this function creates classes and returns their features in a prototype. The number K of classes is user defined.
Algorithm :
1. The nodes are initialized (one node for each class).
2. Choose one object.
3. Search the node which is the best match for this object. This node is the winner.
4. Refine the nodes that are excited by the winner.
Steps 2, 3 and 4 are repeated until all the objects have been chosen at least twice and at least 'iterationnb' have been done. The result prototype is formed with the nodes of the last iteration. Note that the distance used is the euclidean one.