HW2 Mining Image Labels from Web Text Descriptions for Classification
Due Oct 10, 11:59pm

In this homework you will train classifiers for color based visual attributes. Training images will automatically be 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.


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

Part 1 - Mining Image Labels from Descriptions

We will be training 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

Part 3 - Training Classifiers

In this part of the homework you will use RBF kernel SVMs to train attribute classifiers that recognize images displaying a color-based visual characteristic. 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

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

What to turn in

Hand in via email to cse595@gmail.com:

Responses to Q's from students (updated 10/7)