基于视觉显著熵与Object Bank特征的图像记忆性模型

Image memorability model based on visual saliency entropy and Object Bank feature

为了提高图像的记忆性预测能力,提出了一种基于视觉显著熵与改进的Object Bank特征的图像记忆性自动预测方法。该方法改进了传统的Object Bank特征,提取图像的视觉显著熵特征,利用支持向量回归机(SVR)训练得到图像的记忆性预测模型。实验结果表明,在预测准确性方面,所提方法比现有的方法的相关系数高出3个百分点。所提出的模型可以应用于图像的记忆性预测、图像检索排序、广告评价分析等方向。

To improve the prediction ability of image memorability, a method for automatically predicting the memorability of an image was proposed by using visual saliency entropy and improved Object Bank feature. The proposed method improved the traditional Object Bank feature and extracted the visual saliency entropy feature. Then a prediction model of image memorability was constructed by using Support Vector Regression （ SVR）. The experimental results show that the correlation eoeffieiency of the proposed method is three percentage higher than the state-of-the-art method. The proposed model can be used in image memorability prediction, image retrieval ranking and advertisement assessment analysis.