Machine Vision

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Classifier scoring system



I am trying to use the custom classfier VI included in the Vision Development Module in 2010. I classifying three different types of images A, B and C and have created different type of feature fectors for them using different momentums and image processing techniques. However, I am able to get a separtaion for the three classes but I find it very confusing the way the classifier is scored. I need to understand it so that I can compare the different feature vectors that I have created and pick the best one. I have tried SVM and Nearest Neighbor classifiers and I also want to compare their performance.


Using the IMAQ Classifier Accuracy VI I am able to get some scores out. But I would appreciate if someone could explain more deeply the theoretical meaning of these scores:

- Class Predictive Value

- Classification Distribution

- Class Distance Table

- Accuracy


Thanks a lot!

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Here is the help file on that.


Class Predictive Value;

Proportion of samples identified correctly per class.


Classification Distribution;

Table of classification results. Is this what you mean by class distance table?



Proportion of samples classified correctly out of all samples.


If you have more questions, let me know. Hope that helps!

John B.
Embedded Networks R&D
National Instruments
Certified LabVIEW Developer
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