THESIS
2017
ix, 42 pages : illustrations ; 30 cm
Abstract
Human visual attention is subjective and biased according to the personal preference of the viewer,
however, current works of saliency detection are general and objective, without counting the factor
of the observer. This will make the attention prediction for a particular person not accurate enough.
In this work, we propose PANet, a convolutional network that predicts saliency in images with
personal preference. The model consists of two streams which share common feature extraction
layers, and one stream is responsible for saliency prediction, while the other is adapted from the
detection model and used to fit user preference. Experimental results on augmented PASCAL-S
and SALICON dataset confirm that PANet can predict saliency areas according to input preference
vectors. Comp...[
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Human visual attention is subjective and biased according to the personal preference of the viewer,
however, current works of saliency detection are general and objective, without counting the factor
of the observer. This will make the attention prediction for a particular person not accurate enough.
In this work, we propose PANet, a convolutional network that predicts saliency in images with
personal preference. The model consists of two streams which share common feature extraction
layers, and one stream is responsible for saliency prediction, while the other is adapted from the
detection model and used to fit user preference. Experimental results on augmented PASCAL-S
and SALICON dataset confirm that PANet can predict saliency areas according to input preference
vectors. Compared with other general saliency prediction models, the model with ability of fitting
user preference will provide more benefits to either augmented reality (AR) or recommendation
applications.
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