1. Textural Features for Image Classification An introduction
Carlos Andre Braile Przewodowski Filho

2. Summary Image classification Textural Features
Gray-Level Co-Occurrence Matrices
Statistical Features
Discussion

3. Image Classification 1 of 11
Source:
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4. Image Classification Feature Extraction Machine Learning Algorithm
Class/Label
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5. Textural Features Texture 3 of 11 Source: https://goo.gl/mpvb7f

6. Textural Features About the paper
Authors: Robert M. Haralick and K. Shanmugam
Title: Textural Features for Image Classification
Year: 1973
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7. Textural Features Steps Input (Quantize) Input Range (GLCM)
Compute Transitions
(Descriptor)
Features Extraction
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8. Textural Features
Step 1 - Quantization
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9. Step 2 - Gray-Level Co-Occurrence Matrices (GLCM)
Textural Features
Gray-Level Co-Occurrence Matrices
Step 2 - Gray-Level Co-Occurrence Matrices (GLCM)
Table of transitions between quantized values
Provide valuable statistical features
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10. Directions on centralized pixel
Textural Features
Gray-Level Co-Occurrence Matrices
How to Compute GLCM
Directions on centralized pixel
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11. Textural Features Gray-Level Co-Occurrence Matrices
Step 2 - Compute GLCM
Horizontal GLCM
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12. Step 3 - Statistical Features (After Normalization)
Textural Features
Statistical Features
Step 3 - Statistical Features (After Normalization)
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13. Discussion Provided features will feed a ML algorithm
Not rotation invariant
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