  In this paper we propose a strategy for semi-supervised image classification
that leverages unsupervised representation learning and co-training. The
strategy, that is called CURL from Co-trained Unsupervised Representation
Learning, iteratively builds two classifiers on two different views of the
data. The two views correspond to different representations learned from both
labeled and unlabeled data and differ in the fusion scheme used to combine the
image features. To assess the performance of our proposal, we conducted several
experiments on widely used data sets for scene and object recognition. We
considered three scenarios (inductive, transductive and self-taught learning)
that differ in the strategy followed to exploit the unlabeled data. As image
features we considered a combination of GIST, PHOG, and LBP as well as features
extracted from a Convolutional Neural Network. Moreover, two embodiments of
CURL are investigated: one using Ensemble Projection as unsupervised
representation learning coupled with Logistic Regression, and one based on
LapSVM. The results show that CURL clearly outperforms other supervised and
semi-supervised learning methods in the state of the art.
