论文标题
群集显着性预测
Clustered Saliency Prediction
论文作者
论文摘要
我们提出了一种用于图像显着性预测的新方法，簇状的显着性预测。该方法根据其个人特征和已知的显着图将受试者划分为集群，并生成了以群集标签为条件的图像显着性模型。我们在个性化显着性图的公共数据集上测试我们的方法，并使用选定的重要性权重以个人特征因素聚类。我们提出了多域显着转化模型，该模型使用图像刺激和普遍显着图来预测每个群集的显着性图。为了获得普遍的显着图，我们应用了各种最新方法，Deepgaze IIE，ML-NET和SALGAN，并比较了它们在系统中的有效性。我们表明，我们的群集显着性预测技术优于普遍显着性预测模型。另外，我们通过使用我们的算法获得的簇和某些基线方法比较聚类显着性预测的结果来证明聚类方法的有效性。最后，我们提出了一种方法，将新朋友分配到最合适的集群中，并证明其在实验中的用处。
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.
