[ammai2016] A Bayesian Hierarchical Model for Learning Natural Scene Categories

Date: April 7th, 2016

Title: A Bayesian Hierarchical Model for Learning Natural Scene Categories

Author: Fei Fei Li

Novelties:

Previous works about nature scene categorization need experts to label the training data mostly.

The paper introduces an unsupervised way to reach the same goal.

Contributions:

There are three main contributions about this work:
1. The algorithm provides a way to learn scenes without supervision.
2. The algorithm framework is flexible.
3. The algorithm can group these categories into a sensible hierarchy, just like humans.

Technical Summarizes:

The main idea is to classify a scene by extracting its features, representing the image into bag of codewords (i.e. local patches), learning Bayesian hierarchical models, and deciding which category has the highest likelihood probability.
The flow chart is:

They describe patches with local features instead of global features. Previous works on nature scene focused on the latter mostly, but they show that the former is more robust to spatial variations and occlusions.

These is the codebook obtained in their work. Most of the codewords are simple orientations and illumination patterns, this property is similar to human visual system's,

Experiments:

Their dataset contains 13 categories with hundreds of images each, randomly select 100 images from each categories for raining.

By branching the categories  with distance measure between models, it shows the dendrogram, we can figure out that the closest models on the leftmost are all in-door scenes.