The traditional salient object detection models can be divided into several classes based on the low-level features of images and contrast between the pixels. This paper proposes an adversarial learning model (ALM) that includes the generative model and discriminative model. The ALM uses the original image as an input of the generative model to extract the high-level features and forms an initial salient map. Then, the discriminative model is utilized to compare differences in the features between the initial salient map and the ground truth, and the obtained differences are sent to the convolutional layers of the generative model to adjust the parameters for the generative model updating. Due to the serial-iterative adjustment, the salient map of the generative model becomes more similar to the ground truth. Lastly, the ALM forms the salient map fused with the super-pixels by enhancing the color and texture features, so the final salient map is obtained. The ALM is not limited to the color and texture features; on the contrary, it fuses multiple features and achieves good results in the salient target extraction. The experimental results show that ALM performs better than the other ten state-of-the-art models on three different datasets. Thus, the proposed ALM is widely applicable to the salient target extraction.

中文翻译：

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一种基于对抗学习模型的新型显着性检测算法


传统的显着目标检测模型可以根据图像的低级特征和像素之间的对比度分为几类。本文提出了一种包括生成模型和判别模型的对抗性学习模型（ALM）。 ALM使用原始图像作为生成模型的输入来提取高级特征并形成初始显着图。然后，利用判别模型比较初始显着图和地面实况之间的特征差异，并将获得的差异发送到生成模型的卷积层以调整生成模型更新的参数。由于串行迭代调整，生成模型的显着图变得更加类似于地面实况。最后，ALM通过增强颜色和纹理特征，形成与超像素融合的显着图，从而得到最终的显着图。 ALM不限于颜色和纹理特征；相反，它融合了多种特征，在显着目标提取中取得了良好的效果。实验结果表明，ALM 在三个不同数据集上的表现优于其他十种最先进的模型。因此，所提出的ALM广泛适用于显着目标提取。