We present a new method for image salience prediction, Clustered Saliency Prediction. This method divides subjects into clusters based on their personal features and their known saliency maps, and generates an image salience model conditioned on the cluster label. We test our approach on a public dataset of personalized saliency maps and cluster the subjects using selected importance weights for personal feature factors. We propose the Multi-Domain Saliency Translation model which uses image stimuli and universal saliency maps to predict saliency maps for each cluster. For obtaining universal saliency maps, we applied various state-of-the-art methods, DeepGaze IIE, ML-Net and SalGAN, and compared their effectiveness in our system. We show that our Clustered Saliency Prediction technique outperforms the universal saliency prediction models. Also, we demonstrate the effectiveness of our clustering method by comparing the results of Clustered Saliency Prediction using clusters obtained by our algorithm with some baseline methods. Finally, we propose an approach to assign new people to their most appropriate cluster and prove its usefulness in the experiments.

翻译：我们提出了一种新的图像显著性预测方法——聚类显著性预测。该方法根据个体的个人特征及其已知的显著性图将受试者划分为不同聚类，并基于聚类标签生成图像显著性模型。我们在一个包含个性化显著性图的公共数据集上测试了该方法，并采用基于个人特征因子选定重要性权重的策略对受试者进行聚类。我们提出了多域显著性翻译模型，该模型利用图像刺激和通用显著性图来预测每个聚类的显著性图。为获取通用显著性图，我们应用了多种最先进的方法，包括DeepGaze IIE、ML-Net和SalGAN，并比较了它们在本系统中的有效性。实验表明，我们的聚类显著性预测技术优于通用显著性预测模型。此外，我们通过将算法生成的聚类结果与若干基线方法进行对比，验证了聚类方法的有效性。最后，我们提出了一种将新个体分配到最合适聚类的方案，并通过实验证明了其实用价值。