  A deep feature based saliency model (DeepFeat) is developed to leverage the
understanding of the prediction of human fixations. Traditional saliency models
often predict the human visual attention relying on few level image cues.
Although such models predict fixations on a variety of image complexities,
their approaches are limited to the incorporated features. In this study, we
aim to provide an intuitive interpretation of convolu- tional neural network
deep features by combining low and high level visual factors. We exploit four
evaluation metrics to evaluate the correspondence between the proposed
framework and the ground-truth fixations. The key findings of the results
demon- strate that the DeepFeat algorithm, incorporation of bottom up and top
down saliency maps, outperforms the individual bottom up and top down approach.
Moreover, in comparison to nine 9 state-of-the-art saliency models, our
proposed DeepFeat model achieves satisfactory performance based on all four
evaluation metrics.
