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Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

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Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning
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Multi-Block Local Binary Pattern || Calculate LBP Corner Pixel Values || 
https://youtu.be/o8qfJWQ_FG0

 Local Binary Patterns (LBP) is a simple and efficient technique used in image processing to describe the texture or patterns within an image. LBP is widely used for applications like face recognition and texture classification since it is easy to compute and very effective at capturing the texture in photos. Step How LBP WORKS:

  1.  For each pixel in the image, LBP looks at the pixel’s neighbors, typically the 8 pixels surrounding it in a 3x3 grid.
  2. LBP compares each of these neighboring pixels with the center pixel. If the neighboring pixel has a value greater than or equal to the center pixel, it's marked as 1; otherwise, it's marked as 0. This comparison forms a binary number for the pixel.
  3.  The binary number is then converted into a decimal value. This value represents the texture pattern at that pixel.
  4. By doing this for every pixel in the image, LBP creates a new image that highlights the texture information.
Difference Between LBP and MB-LBP 

  • LBP compares individual pixels to a center pixel in a small neighborhood (usually 3x3).
  • MB-LBP compares the average intensity of blocks (groups of pixels) to the average intensity of a center block. (Divide into Blocks, Compare Block Averages, Create Binary Pattern and Convert to Decimal)

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