A deep feature-based saliency model (DeepFeat) is developed to leverage understanding of the prediction of human fixations. Conventional saliency models often predict the human visual attention relying on few image cues. Although such models predict fixations on a variety of image complexities, their approaches are limited to the incorporated features. In this paper, we aim to utilize the deep features of convolutional neural networks by combining bottom-up (BU) and top-down (TD) saliency maps. The proposed framework is applied on deep features of three popular deep convolutional neural networks (DCNNs). We exploit four evaluation metrics to evaluate the correspondence between the proposed saliency model and the ground-truth fixations over two datasets. The results demonstrate that the deep features of pretrained DCNNs over the ImageNet dataset are strong predictors of the human fixations. The incorporation of BU and TD saliency maps outperforms the individual BU or TD implementations. Moreover, in comparison to nine saliency models, including four state-of-the-art and five conventional saliency models, our proposed DeepFeat model outperforms the conventional saliency models over all four evaluation metrics.

中文翻译：

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DeepFeat：一种基于卷积神经网络深度特征的自下而上和自上而下的显着性模型

开发了一种基于深度特征的显着性模型 (DeepFeat)，以利用对人类注视预测的理解。传统的显着性模型通常依靠很少的图像线索来预测人类的视觉注意力。尽管此类模型预测对各种图像复杂性的注视，但它们的方法仅限于合并的特征。在本文中，我们旨在通过结合自底向上（BU）和自顶向下（TD）显着图来利用卷积神经网络的深层特征。所提出的框架应用于三种流行的深度卷积神经网络 (DCNN) 的深度特征。我们利用四个评估指标来评估所提出的显着性模型与两个数据集上的地面实况固定之间的对应关系。结果表明，预训练 DCNN 在 ImageNet 数据集上的深层特征是人类注视的强预测器。BU 和 TD 显着图的结合优于单个 BU 或 TD 实现。此外，与九个显着性模型（包括四个最先进的和五个传统显着性模型）相比，我们提出的 DeepFeat 模型在所有四个评估指标上都优于传统显着性模型。