Textural Features for Image Classification An introduction

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Presentation transcript:

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

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

Image Classification 1 of 11 Source: https://goo.gl/4GjCJu 1 of 11

Image Classification Feature Extraction Machine Learning Algorithm Class/Label 2 of 11

Textural Features Texture 3 of 11 Source: https://goo.gl/mpvb7f

Textural Features About the paper Authors: Robert M. Haralick and K. Shanmugam Title: Textural Features for Image Classification Year: 1973 4 of 11

Textural Features Steps Input (Quantize) Input Range (GLCM) Compute Transitions (Descriptor) Features Extraction 5 of 11

Textural Features Step 1 - Quantization 6 of 11

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 7 of 11

Directions on centralized pixel Textural Features Gray-Level Co-Occurrence Matrices How to Compute GLCM Directions on centralized pixel 8 of 11

Textural Features Gray-Level Co-Occurrence Matrices Step 2 - Compute GLCM Horizontal GLCM 9 of 11

Step 3 - Statistical Features (After Normalization) Textural Features Statistical Features Step 3 - Statistical Features (After Normalization) 10 of 11

Discussion Provided features will feed a ML algorithm Not rotation invariant 11 of 11