Evaluation of bottom-up saliency model using deep features pretrained by deep convolutional neural networks
Ali Mahdi, Jun Qi Qin · Journal of Electronic Imaging · 2019
We present extensive evaluations of deep features pretrained by state-of-the-art deep convolutional neural networks (DCNNs) for predictions of human fixations. The evaluations are conducted using a bottom-up saliency model, which utilizes deep features of DCNNs pretrained for object classification. Using various selections of deep feature maps, 35 implementations of the bottom-up saliency model are computed, evaluated, and compared over three publicly available datasets using four evaluation metrics. The experimental results demonstrate that the pretrained deep features are strong predictors of human fixations. The incorporation of multiscale deep feature maps benefits the saliency prediction. The depth of DCNNs has a negative effect on saliency prediction. Moreover, we also compare the performance of the proposed deep features-based bottom-up saliency model with the other eight bottom-up saliency models. The comparison results show that our saliency model can outperform other conventional bottom-up saliency models.