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LEARNING SALIENCY FOR HUMAN ACTION RECOGNITION
PhDWhen we are looking at a visual stimuli, there are certain areas that stand out
from the neighbouring areas and immediately grab our attention. A map that identi-
es such areas is called a visual saliency map. As humans can easily recognize actions
when watching videos, having their saliency maps available might be bene cial for
a fully automated action recognition system. In this thesis we look into ways of
learning to predict the visual saliency and how to use the learned saliency for action
recognition.
In the rst phase, as opposed to the approaches that use manually designed fea-
tures for saliency prediction, we propose few multilayer architectures for learning
saliency features. First, we learn rst layer features in a two layer architecture using
an unsupervised learning algorithm. Second, we learn second layer features in a two
layer architecture using a supervision from recorded human gaze xations. Third, we
use a deep architecture that learns features at all layers using only supervision from
recorded human gaze xations.
We show that the saliency prediction results we obtain are better than those
obtained by approaches that use manually designed features. We also show that
using a supervision on higher levels yields better saliency prediction results, i.e. the
second approach outperforms the rst, and the third outperforms the second.
In the second phase we focus on how saliency can be used to localize areas that will
be used for action classi cation. In contrast to the manually designed action features,
such as HOG/HOF, we learn the features using a fully supervised deep learning
architecture. We show that our features in combination with the predicted saliency
(from the rst phase) outperform manually designed features. We further develop
an SVM framework that uses the predicted saliency and learned action features to
both localize (in terms of bounding boxes) and classify the actions. We use saliency
prediction as an additional cost in the SVM training and testing procedure when
inferring the bounding box locations. We show that the approach in which saliency
cost is added yields better action recognition results than the approach in which the
cost is not added. The improvement is larger when the cost is added both in training
and testing, rather than just in testing
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