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Build a classifier to classify transport using sift and svm

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Classification-SIFT-BoW-SVM

Build a classifier to classsification transport using sift, bag of words and svm

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Root/

data  # Data contains total 1854 image of 5 classes: bus, car, moto, pedestrian 

data_loader.py  # Imread image and label for data

sift_extractors.py # Extract features with SIFT and Build Bag of Word

build_model.py  # Build model SVM and training

gridSearchCV    # implement GridSearchCV find Hyper-params

Hyperparameters

  • Number of centroid in Kmean: 60
  • Penalty parameter C of the error term in SVM: 30

Label

  • 'moto': 0
  • 'car': 1
  • 'pedestrian': 2
  • 'truck': 3
  • 'bus': 4

Statistic Data

- Statistics All Classes

- Statistics Classes

Accuracy

- Accuracy score: 72.9% - test size: 0.2 

Using GridSearchCV

- Best params

    'clf__C': 5,
    'clf__gamma': 0.01,
    'clf__kernel': 'rbf'
    
 - Accuracy:  75.72% +/-1.52%

Requirements

- opencv-contrib-python==3.4.2.16

- opencv-python==3.4.2.16

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