Bag of Words

4 important questions on Bag of Words

Nearest Neighbour Search algorithm:

  1. Compute local features in each image independently
  2. "Label" each feature by a descriptor vector based on its intensity
  3. Finding corresponding features is transformed to finding nearest neighbour vectors
  4. Rank matched images by number of corresponding regions

What is Bag of Words?

- Image classification, by treating image features as words.

- Orderless document representation: frequencies of words from a dictionary

- Count the number of vectors and compare them with other images

Learning the visual vocabulary algorithm:

- Feature extraction
  • Regular grid
  • Interest points (SIFT)
- Plot them in a graph
- Create cluster
- Find the mean cluster
  • Higher grades + faster learning
  • Never study anything twice
  • 100% sure, 100% understanding
Discover Study Smart

What are K-means alternatives?

  1. Hierarchical clustering
  2. Spectral clustering
  3. Vocabulary trees

The question on the page originate from the summary of the following study material:

  • A unique study and practice tool
  • Never study anything twice again
  • Get the grades you hope for
  • 100% sure, 100% understanding
Remember faster, study better. Scientifically proven.
Trustpilot Logo