1. Using Transductive SVMs for Object Classification in Images
Courtenay Cotton
December 11, 2007
COMS 6772: Adv. Machine Learning

2. Model Overview
Image pixels are filtered to generate n-dimensional feature vectors
Pixel feature vectors are quantized (k-means) into distinct “words” in a codebook
Regions (objects) in images are modeled by their histograms over the words in the dictionary

3. Motivation for Using Transduction
Using a hand-labeled dataset: very time-consuming
Would be nice to take advantage of many unlabeled images
Might have to be automatically segmented, but rough segmentation would be ok
Reference papers on transduction for text classification – model shares some similar features:
High dimensional input space (many code words)
Sparsity (most regions contain only a few words)
Few irrelevant features (all words appear in at least one region, and may be key in identifying it)

4. Experiments Dataset: SVM: 300-word code book 9 classes
437 test regions
432 training regions – varied percent of labeled vs. unlabeled
SVM:
SVMlight package with transduction
Created 9 one-vs-rest classifiers, at each level of labeling
Had to increase cost factor on positive examples (due to lopsidedness of data)
To test, ran each classifier and chose class with highest prediction

5. Results
Transductive SVM shows definite improvement at all levels of data labeling
SVM predictably works better than baseline classifier

6. Thank You
Questions?