Assignment 2. Classification of Photographs using SVM
Due: March 25th, 2011
In this assignment, we will use Support Vector Machine (SVM) to do
some classification of photographs. The software package we will
use is LibSVM.
On their webpage, download the version
for Matlab for this assignment.
Part 1. Beach or Grassland (5%) In this part, you need
to use SVM to classify the photographs into "beach" or
"grassland". Here are 120 photographs
of "beach", and here are 120
photograhs of "grassland". Please random select half of the data
for training, and the rest of the data for testing.
The feature vector you will use is called "Grid-based Color
Moment". Here is how to compute this feature vector for a given
- Convert the image from RGB fro HSV color space (Hint: use
the function rgb2hsv in Matlab for this operation)
- Uniformly divide the image into 3x3 blocks
- For each of these nine blocks
- Each block will have 3+3+3=9 features, and thus the entire
image will have 9x9=81 features. Before we use SVM to train
the classifier, we first need to normalize the 81 features to
be within the same range, in order to achieve good numerical
behavior. To do the normalization, for each of the
- Now, use LibSVM to train your classifier based on the
normalized feature vectors of the training
- Once the training is done, apply the trained classifier on the
testing dataset. Make sure to report both your training error and
testing error on your webpage.
Part 2. Try Your Favoriate Photograph Classification Task
(5%) This is the part to be creative. Think of some
interesting classification tasks for photographs, and also think
of some feature vector that you expect to be useful for solving
your classification task. You also need to find the images to
perform the classification.
Here are some possible classification tasks:
- Indoor / Outdoor photographs
- Photographs / Paintings
- High quality / Low quality (i.e. blurred, noisy) images
Here are some possible features you might want to look into:
- Higher order color moment, such as kurtosis.
- Color Histogram (i.e., a 3D histogram, either in RGB space
or HSV space. Typically discretize into 8x8x8=512 bins.)
- Edge Histogram (i.e., a 2D histogram. One dimension is the
strength of the edge, and the other dimension is the
orientation of the edge).
- Structural Similarity Index Measure (SSIM)
How to Submit Your Assignment?
You should create a webpage for showing your results and
explaining what you did. Do not modify the webpage after the
due date. Send an email to Oliver Cossairt <firstname.lastname@example.org> with the URL to your webpage before the