Super Diffusion for Salient Object Detection
Super Diffusion for Salient Object Detection
复制标题
用于显着物体检测的超级扩散
DOI：
10.1109/tip.2019.2954209
复制
发表时间：
2020
影响因子：
10.6
通讯作者：
Peng Jingliang
中科院分区：
文献类型：
--
作者：
Jiang Peng;Pan Zhiyi;Tu Changhe;Vasconcelos Nuno;Chen Baoquan;Peng Jingliang
显著性目标检测方法的一个主要分支是基于扩散的方法，该方法在给定图像上构造图模型，并通过扩散矩阵将种子显著性值扩散到整个图。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扩散通用机制下显著对象检测的鲁棒性和准确性。在这项工作中，我们首先提出了一个新的观点的扩散过程的工作机制的基础上的数学分析，这表明扩散过程实际上是计算节点的相似性相对于种子的基础上扩散地图。在此分析之后，我们提出了超扩散，一种新的包容性的基于学习的框架，用于显着对象检测，它通过整合大量的特征空间，尺度，甚至是最初为非扩散为基础的显着对象检测计算的特征，从而实现了最佳和鲁棒的性能。通过监督学习确定用于集成的最优参数的封闭形式的解决方案。在地方一级，我们建议在整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，我们建议从最具鉴别力的特征向量和恒定特征向量（用于显著性归一化）重新合成每个个体扩散矩阵。所提出的框架实施和实验上prehistoricused基准数据集，一贯导致国家的最先进的性能。
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.