Programming
principal component analysis VS Linear discriminant analysis template Matching

Question Description

I’m studying and need help with a Programming question to help me learn.

Hey folks

im doing a project with image matching. i wanna match image i meant input a image and find exact matching in database.

So i wanna know in Image Matching what is the best techniques between principal component analysis VS Linear discriminant analysis template Matching and Eigen based matching also Feature-based method for matching


i have read definition and some research paper about these algorithms but i cant understand which is the best method in my scenario. i meant these techniques(principal component analysis VS Linear discriminant analysis template Matching and Eigen based matching also Feature-based method for matching) i wanna know the exact usage (when to use )of these algorithms.

All research papers mentioned these techniques are for matching and these techniques use every where. so im little bit confused.

So please can anyone tell me WHAT IS THE EXACT DIFFERENCE OF THESE METHOD AND WHEN TO USE and pros and cons. and justifications

thank you.


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Final Answer

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In most cases, the discipline is self-governed by the entities which require the programming, and sometimes very strict environments are defined

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fitting k (56)
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