/Small_sample_recognition

Small sample computer motherboard recognition.

Primary LanguagePython

Small_sample_recognition

Small sample computer motherboard recognition.

Principle

The model is based on the sliding window BOF algorithm.

Algorithm framework

  1. Feature extraction: Use SIFT for feature extraction.
  2. Vocabulary construction: K-means is used for clustering. Follow-up tests show that taking 1024 classes works best.
  3. Category judgment: After clustering, we get Bag of visual words histograms. Use SVM as discriminative classifier.
  4. Generate bounding box: Reduce the sliding step and window size, and merge the small bounding boxes into larger bounding boxes to make the windows more accurate and reasonable in size.