principal component analysis VS Linear discriminant analysis VS template Matching and Eigen based matchign [closed]

asked 2013-07-09 08:16:27 -0500

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

What is the bet method and Pros and cons and reason

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.

thank you

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Closed for the following reason not a real question by SR
close date 2013-07-09 16:18:39.274441

Comments

There is no exact rule to define what technique to use. Basically try them all out and see which wotks best in your application. Also please use the search function of this forum. Your question has been answered many times before...

StevenPuttemans gravatar imageStevenPuttemans ( 2013-07-09 10:00:16 -0500 )edit

Horrible style. Use punctuation. Use grammatically correct sentences. Ask a clearly defined question. Closed.

SR gravatar imageSR ( 2013-07-09 16:18:03 -0500 )edit