Bag of Visual Words for Image Classification

Classifying cats, chairs and cameras with SIFT, KMeans, KNN & SVM.

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Domain
Computer Vision
Year
2025
Type
Research report — internal engineering work

This is internal engineering and R&D work, not a client project. It is published to show technical depth — the modelling, evaluation, and tooling behind the AI automation we build for clients. No client is associated with it.

A Bag of Visual Words (BoVW) pipeline for image classification using handcrafted SIFT descriptors, KMeans clustering, and KNN/SVM classifiers, classifying images of three object categories: cats, chairs, and cameras.

Tools & methods

  • Python
  • OpenCV
  • SIFT
  • KMeans
  • KNN & SVM
  • Matplotlib
Full technical report

Objectives

  • Build a robust feature-extraction pipeline using SIFT.
  • Apply KMeans to create a visual vocabulary.
  • Train KNN and SVM models to classify unseen test images.
  • Visualize histogram representations and predictions.

Process

  • Load images and convert to grayscale; extract SIFT keypoints and descriptors into a feature pool.
  • Cluster all descriptors with KMeans (k = 200) — each centre becomes a visual word.
  • Encode each image as a histogram of visual words.
  • Train KNN (k = 5) and a linear SVM on the histograms, then predict on unseen images.

Results

  • SVM outperformed KNN — Cat 90%, Chair 82%, Camera 95%.
  • Histogram encoding drastically reduced image complexity.
  • Visualizations helped debug class confusion.

What we took from it

  • BoVW works effectively with SIFT and simple classifiers.
  • Clustering + classical ML can build powerful vision systems.

From the report

  • Bag-of-Visual-Words classification result (1)
    Bag-of-Visual-Words classification result (1)
  • Bag-of-Visual-Words classification result (2)
    Bag-of-Visual-Words classification result (2)

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