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A deep feature based saliency model (DeepFeat) is developed to leverage theunderstanding of the prediction of human fixations. Traditional saliency modelsoften 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, weaim to provide an intuitive interpretation of convolu- tional neural networkdeep features by combining low and high level visual factors. We exploit fourevaluation metrics to evaluate the correspondence between the proposedframework and the ground-truth fixations. The key findings of the resultsdemon- strate that the DeepFeat algorithm, incorporation of bottom up and topdown saliency maps, outperforms the individual bottom up and top down approach.Moreover, in comparison to nine 9 state-of-the-art saliency models, ourproposed DeepFeat model achieves satisfactory performance based on all fourevaluation metrics.

DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets