In this paper, we propose a deep multimodal feature learning (DMFL) network for RGB-D salient object detection. The color and depth features are firstly extracted from low level to high level feature using CNN. Then the features at the high layer are shared and concatenated to construct joint feature representation of multi-modalities. The fused features are embedded to a high dimension metric space to express the salient and non-salient parts. And also a new objective function, consisting of cross-entropy and metric loss, is proposed to optimize the model. Both pixel and attribute level discriminative features are learned for semantical grouping to detect the salient objects. Experimental results show that the proposed model achieves promising performance and has about 1% to 2% improvement to conventional methods.

在本文中，我们提出了一种用于RGB-D显着物体检测的深度多峰特征学习（DMFL）网络。首先使用CNN从低级到高级特征提取颜色和深度特征。然后高层的特征被共享和连接以构造多模态的联合特征表示。融合特征嵌入到高维度量空间中，以表示突出部分和非突出部分。并提出了由交叉熵和度量损失组成的新目标函数，对模型进行了优化。学习像素和属性级别的判别特征，以进行语义分组以检测突出的对象。实验结果表明，提出的模型具有良好的性能，与常规方法相比，具有约1％至2％的改进。