One major branch of saliency object detection methods are diffusion-based which construct a graph model on a given image and diffuse seed saliency values to the whole graph by a diffusion matrix. While their performance is sensitive to specific feature spaces and scales used for the diffusion matrix definition, little work has been published to systematically promote the robustness and accuracy of salient object detection under the generic mechanism of diffusion. In this work, we firstly present a novel view of the working mechanism of the diffusion process based on mathematical analysis, which reveals that the diffusion process is actually computing the similarity of nodes with respect to the seeds based on diffusion maps. Following this analysis, we propose super diffusion, a novel inclusive learning-based framework for salient object detection, which makes the optimum and robust performance by integrating a large pool of feature spaces, scales and even features originally computed for non-diffusion-based salient object detection. A closed-form solution of the optimal parameters for the integration is determined through supervised learning. At the local level, we propose to promote each individual diffusion before the integration. Our mathematical analysis reveals the close relationship between saliency diffusion and spectral clustering. Based on this, we propose to re-synthesize each individual diffusion matrix from the most discriminative eigenvectors and the constant eigenvector (for saliency normalization). The proposed framework is implemented and experimented on prevalently used benchmark datasets, consistently leading to state-of-the-art performance.

中文翻译：

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用于显着物体检测的超级扩散。


显着性对象检测方法的一个主要分支是基于扩散的，它在给定图像上构建图模型，并通过扩散矩阵将种子显着性值扩散到整个图。虽然它们的性能对用于扩散矩阵定义的特定特征空间和尺度敏感，但很少有工作发表来系统地提高通用扩散机制下显着目标检测的鲁棒性和准确性。在这项工作中，我们首先基于数学分析提出了扩散过程工作机制的新颖观点，揭示了扩散过程实际上是基于扩散图计算节点相对于种子的相似度。根据这一分析，我们提出了超级扩散，这是一种新颖的基于包容性学习的显着目标检测框架，它通过集成大量特征空间、尺度甚至最初为非基于扩散的显着计算的特征来实现最佳和鲁棒的性能物体检测。通过监督学习确定集成最佳参数的封闭式解。在地方层面，我们建议在融合之前促进每个个体的扩散。我们的数学分析揭示了显着性扩散和谱聚类之间的密切关系。基于此，我们建议从最具辨别力的特征向量和恒定特征向量（用于显着性归一化）重新合成每个单独的扩散矩阵。所提出的框架在常用的基准数据集上进行了实施和实验，始终保持最先进的性能。