HW2 Mining Image Labels from Web Text Descriptions for Classification
Due March 6, 11:59pm

In this homework you will train classifiers for color based visual attributes. Training images will be automatically labeled by mining the text descriptions associated with web shopping imges. You will then use these classifiers to retrieve images displaying each attribute from a collection of testing images.

Data

We will again use shopping images, this time the bag portion of the dataset -- bags.tar.gz.

Part 1 - Mining Image Labels from Descriptions (15 points)

We will be training binary attribute classifiers for 5 color terms ("black", "brown", "red", "silver", and "gold"). In this part of the homework you will automatically collect training and testing images from the bag dataset by utilizing their existing associated text descriptions.

Part 2 - Computing Image Descriptors (10 points)

Part 3 - Training Classifiers (25 points)

In this part of the homework you will train RBF kernel SVMs to recognize images displaying a color-based visual attributes (note you should train 5 binary SVMs, 1 for each attribute term). Positive examples for training an attribute, e.g. "black", will consist of those images in your training set that have the attribute in their text description. Negative examples will be the rest of the images in your training set.

Part 4 - Classifying/Retrieving Images without Attribute Annotations (20 points)

Here we will retrieve images displaying visual attributes from your testing set (described in Part 1).

Part 5 - Freestyle (30 points)

What to turn in

Hand in via email to unc.790.133@gmail.com: