基于一种改进的跨层级特征融合的循环全卷积神经网络,提出了一种结合深度学习的图像显著目标检测算法。通过改进的深度卷积网络模型对输入图像进行特征提取,利用跨层级联合框架进行特征融合,生成了高层语义特征的初步显著图;将初步显著图与图像底层特征融合进行显著性传播以获取结构信息;利用条件随机场对显著性传播结果进行优化,得到了最终显著图。利用大型数据集将所提算法与其他多种算法进行了测试对比,研究结果表明,在对复杂场景图像的显著目标检测方面,所提算法稳健性更好,显著目标检测的完整性提升,背景得到了更有效的抑制。

An algorithm of image salient object detection combined with deep learning is proposed based on an improved recurrent deep convolutional neural network with the cross-level feature fusion. The feature extraction of input images is performed through this improved recurrent deep convolutional neural network model. The cross-level joint framework is used for the feature fusion and thus the initial salient maps with high-level semantics features are generated. The saliency propagation is applied to the fusion of initial salient maps and low-level image features, and thus the structural information is obtained. The saliency propagation results are further optimized with the conditional random field and the final salient maps are realized. With the massive datasets, the proposed algorithm is tested and compared with other algorithms. The research results show that the proposed method is more robust than the existing algorithms in the image salient object detection of the complex scenes. Moreover, the integrity of the significant target detection is improved and the background is suppressed effectively.