Transcript courtenaycotton.ppt

Using Transductive SVMs for Object
Classification in Images
Courtenay Cotton
December 11, 2007
COMS 6772: Adv. Machine Learning
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
Motivation for Using Transduction
Using a hand-labeled dataset: very time-consuming
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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:
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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)
Experiments
Dataset:
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300-word code book
9 classes
437 test regions
432 training regions – varied percent of labeled vs. unlabeled
SVM:
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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
Results
Transductive SVM
shows definite
improvement at all
levels of data
labeling
SVM predictably
works better than
baseline classifier
Thank You
Questions?